@misc{zhang_biomedclip_2025,
	title = {{BiomedCLIP}: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs},
	shorttitle = {{BiomedCLIP}},
	url = {http://arxiv.org/abs/2303.00915},
	doi = {10.48550/arXiv.2303.00915},
	urldate = {2025-02-25},
	publisher = {arXiv},
	author = {Zhang, Sheng and Xu, Yanbo and Usuyama, Naoto and Xu, Hanwen and Bagga, Jaspreet and Tinn, Robert and Preston, Sam and Rao, Rajesh and Wei, Mu and Valluri, Naveen and Wong, Cliff and Tupini, Andrea and Wang, Yu and Mazzola, Matt and Shukla, Swadheen and Liden, Lars and Gao, Jianfeng and Crabtree, Angela and Piening, Brian and Bifulco, Carlo and Lungren, Matthew P. and Naumann, Tristan and Wang, Sheng and Poon, Hoifung},
	month = jan,
	year = {2025},
	note = {arXiv:2303.00915 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: The models are released at https://aka.ms/biomedclip},
	file = {Preprint PDF:/Users/seowung/Zotero/storage/L42APXAZ/Zhang 등 - 2025 - BiomedCLIP a multimodal biomedical foundation mod.pdf:application/pdf;Snapshot:/Users/seowung/Zotero/storage/MFNPE56R/2303.html:text/html},
}

@misc{lin_pmc-clip_2023,
	title = {{PMC}-{CLIP}: {Contrastive} {Language}-{Image} {Pre}-training using {Biomedical} {Documents}},
	shorttitle = {{PMC}-{CLIP}},
	url = {http://arxiv.org/abs/2303.07240},
	doi = {10.48550/arXiv.2303.07240},
	urldate = {2025-02-25},
	publisher = {arXiv},
	author = {Lin, Weixiong and Zhao, Ziheng and Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
	month = mar,
	year = {2023},
	note = {arXiv:2303.07240 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Multimedia},
	annote = {Comment: 10 pages, 3 figures},
	file = {Preprint PDF:/Users/seowung/Zotero/storage/4G72VSBV/Lin 등 - 2023 - PMC-CLIP Contrastive Language-Image Pre-training .pdf:application/pdf;Snapshot:/Users/seowung/Zotero/storage/2TUIXKG5/2303.html:text/html},
}

@inproceedings{huang_gloria_2021,
	title = {{GLoRIA}: {A} {Multimodal} {Global}-{Local} {Representation} {Learning} {Framework} for {Label}-efficient {Medical} {Image} {Recognition}},
	shorttitle = {{GLoRIA}},
	url = {https://ieeexplore.ieee.org/document/9710099},
	doi = {10.1109/ICCV48922.2021.00391},
	urldate = {2025-02-25},
	booktitle = {2021 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} ({ICCV})},
	author = {Huang, Shih-Cheng and Shen, Liyue and Lungren, Matthew P. and Yeung, Serena},
	month = oct,
	year = {2021},
	note = {ISSN: 2380-7504},
	keywords = {and cell microscopy, biological, Deep learning, Image analysis, Image recognition, Image segmentation, Medical, Neural networks, Representation learning, Training, Transfer/Low-shot/Semi/Unsupervised Learning, Vision + language},
	pages = {3922--3931},
	file = {Full Text PDF:/Users/seowung/Zotero/storage/H6X75K6P/Huang 등 - 2021 - GLoRIA A Multimodal Global-Local Representation L.pdf:application/pdf},
}

@misc{muller_joint_2022,
	title = {Joint {Learning} of {Localized} {Representations} from {Medical} {Images} and {Reports}},
	volume = {13686},
	url = {http://arxiv.org/abs/2112.02889},
	urldate = {2025-02-25},
	author = {Müller, Philip and Kaissis, Georgios and Zou, Congyu and Rueckert, Daniel},
	year = {2022},
	doi = {10.1007/978-3-031-19809-0_39},
	note = {arXiv:2112.02889 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing},
	pages = {685--701},
	annote = {Comment: Accepted at ECCV 2022},
	file = {Preprint PDF:/Users/seowung/Zotero/storage/H54VUSTK/Müller 등 - 2022 - Joint Learning of Localized Representations from M.pdf:application/pdf;Snapshot:/Users/seowung/Zotero/storage/MBCNM24Z/2112.html:text/html},
}

@inproceedings{wang_medclip_2022,
	address = {Abu Dhabi, United Arab Emirates},
	title = {{MedCLIP}: {Contrastive} {Learning} from {Unpaired} {Medical} {Images} and {Text}},
	shorttitle = {{MedCLIP}},
	url = {https://aclanthology.org/2022.emnlp-main.256/},
	doi = {10.18653/v1/2022.emnlp-main.256},
	urldate = {2025-02-25},
	booktitle = {Proceedings of the 2022 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Wang, Zifeng and Wu, Zhenbang and Agarwal, Dinesh and Sun, Jimeng},
	editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue},
	month = dec,
	year = {2022},
	pages = {3876--3887},
	file = {Full Text PDF:/Users/seowung/Zotero/storage/7ASZ5FLU/Wang 등 - 2022 - MedCLIP Contrastive Learning from Unpaired Medica.pdf:application/pdf},
}

@article{shi_retinal_2021,
	title = {Retinal {Vasculopathy} in {Alzheimer}’s {Disease}},
	volume = {15},
	issn = {1662-4548},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493243/},
	doi = {10.3389/fnins.2021.731614},
	urldate = {2025-02-25},
	journal = {Frontiers in Neuroscience},
	author = {Shi, Haoshen and Koronyo, Yosef and Rentsendorj, Altan and Fuchs, Dieu-Trang and Sheyn, Julia and Black, Keith L. and Mirzaei, Nazanin and Koronyo-Hamaoui, Maya},
	month = sep,
	year = {2021},
	pmid = {34630020},
	pmcid = {PMC8493243},
	pages = {731614},
	file = {PubMed Central Full Text PDF:/Users/seowung/Zotero/storage/6XTXQ85P/Shi 등 - 2021 - Retinal Vasculopathy in Alzheimer’s Disease.pdf:application/pdf},
}

@article{zhang_retina_2022,
	title = {The retina: {A} window in which to view the pathogenesis of {Alzheimer}'s disease},
	volume = {77},
	issn = {1872-9649},
	shorttitle = {The retina},
	doi = {10.1016/j.arr.2022.101590},
	language = {eng},
	journal = {Ageing Research Reviews},
	author = {Zhang, Jie and Shi, Lei and Shen, Yong},
	month = may,
	year = {2022},
	pmid = {35192959},
	keywords = {Aged, Alzheimer Disease, Alzheimer’s disease, Animals, Brain, Early Diagnosis, Humans, Mice, Mice, Transgenic, New window, Non-invasive, Preclinical and clinical stages, Retina},
	pages = {101590},
}

@article{koronyo_retinal_2017,
	title = {Retinal amyloid pathology and proof-of-concept imaging trial in {Alzheimer}’s disease},
	volume = {2},
	issn = {0021-9738},
	url = {https://insight.jci.org/articles/view/93621},
	doi = {10.1172/jci.insight.93621},
	language = {en},
	number = {16},
	urldate = {2025-02-25},
	journal = {JCI Insight},
	author = {Koronyo, Yosef and Biggs, David and Barron, Ernesto and Boyer, David S. and Pearlman, Joel A. and Au, William J. and Kile, Shawn J. and Blanco, Austin and Fuchs, Dieu-Trang and Ashfaq, Adeel and Frautschy, Sally and Cole, Gregory M. and Miller, Carol A. and Hinton, David R. and Verdooner, Steven R. and Black, Keith L. and Koronyo-Hamaoui, Maya},
	month = aug,
	year = {2017},
	pmid = {0},
	note = {Publisher: American Society for Clinical Investigation},
	file = {Full Text PDF:/Users/seowung/Zotero/storage/CP753PJ4/Koronyo 등 - 2017 - Retinal amyloid pathology and proof-of-concept ima.pdf:application/pdf},
}

@article{cheung_retinal_2021,
	title = {Retinal imaging in {Alzheimer}’s disease},
	volume = {92},
	copyright = {© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.},
	issn = {0022-3050, 1468-330X},
	url = {https://jnnp.bmj.com/content/92/9/983},
	doi = {10.1136/jnnp-2020-325347},

	language = {en},
	number = {9},
	urldate = {2025-02-25},
	journal = {Journal of Neurology, Neurosurgery \& Psychiatry},
	author = {Cheung, Carol Y. and Mok, Vincent and Foster, Paul J. and Trucco, Emanuele and Chen, Christopher and Wong, Tien Yin},
	month = sep,
	year = {2021},
	pmid = {34108266},
	note = {Publisher: BMJ Publishing Group Ltd
Section: General neurology},
	keywords = {Alzheimer's disease, image analysis, ophthalmology},
	pages = {983--994},
	file = {Full Text PDF:/Users/seowung/Zotero/storage/ETKMLTLJ/Cheung 등 - 2021 - Retinal imaging in Alzheimer’s disease.pdf:application/pdf},
}


@article{zhou_foundation_2023,
	title = {A foundation model for generalizable disease detection from retinal images},
	volume = {622},
	copyright = {2023 The Author(s)},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/s41586-023-06555-x},
	doi = {10.1038/s41586-023-06555-x},
	language = {en},
	number = {7981},
	urldate = {2025-02-26},
	journal = {Nature},
	author = {Zhou, Yukun and Chia, Mark A. and Wagner, Siegfried K. and Ayhan, Murat S. and Williamson, Dominic J. and Struyven, Robbert R. and Liu, Timing and Xu, Moucheng and Lozano, Mateo G. and Woodward-Court, Peter and Kihara, Yuka and Altmann, Andre and Lee, Aaron Y. and Topol, Eric J. and Denniston, Alastair K. and Alexander, Daniel C. and Keane, Pearse A.},
	month = oct,
	year = {2023},
	note = {Publisher: Nature Publishing Group},
	keywords = {Cardiovascular diseases, Medical imaging, Prognosis, Retinal diseases, Translational research},
	pages = {156--163},
	file = {Full Text PDF:/Users/seowung/Zotero/storage/HU5IXUFE/Zhou 등 - 2023 - A foundation model for generalizable disease detec.pdf:application/pdf},
}

@misc{du_ret-clip_2024,
	title = {{RET}-{CLIP}: {A} {Retinal} {Image} {Foundation} {Model} {Pre}-trained with {Clinical} {Diagnostic} {Reports}},
	shorttitle = {{RET}-{CLIP}},
	url = {http://arxiv.org/abs/2405.14137},
	doi = {10.48550/arXiv.2405.14137},
	urldate = {2025-02-26},
	publisher = {arXiv},
	author = {Du, Jiawei and Guo, Jia and Zhang, Weihang and Yang, Shengzhu and Liu, Hanruo and Li, Huiqi and Wang, Ningli},
	month = aug,
	year = {2024},
	note = {arXiv:2405.14137 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Accepted by MICCAI 2024},
	file = {Preprint PDF:/Users/seowung/Zotero/storage/NLZML8KV/Du 등 - 2024 - RET-CLIP A Retinal Image Foundation Model Pre-tra.pdf:application/pdf;Snapshot:/Users/seowung/Zotero/storage/SA5JZTBB/2405.html:text/html},
}

@misc{wu_mm-retinal_2024,
	title = {{MM}-{Retinal}: {Knowledge}-{Enhanced} {Foundational} {Pretraining} with {Fundus} {Image}-{Text} {Expertise}},
	shorttitle = {{MM}-{Retinal}},
	url = {http://arxiv.org/abs/2405.11793},
	doi = {10.48550/arXiv.2405.11793},
	urldate = {2025-02-26},
	publisher = {arXiv},
	author = {Wu, Ruiqi and Zhang, Chenran and Zhang, Jianle and Zhou, Yi and Zhou, Tao and Fu, Huazhu},
	month = may,
	year = {2024},
	note = {arXiv:2405.11793 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Early Accepted by The International Conference on Medical Image Computing and Computer Assisted Intervention(MICCAI)2024},
	file = {Preprint PDF:/Users/seowung/Zotero/storage/KTQCZRHT/Wu 등 - 2024 - MM-Retinal Knowledge-Enhanced Foundational Pretra.pdf:application/pdf;Snapshot:/Users/seowung/Zotero/storage/SAXRW3II/2405.html:text/html},
}

@article{yang_large_2022,
	title = {A large language model for electronic health records},
	volume = {5},
	copyright = {2022 The Author(s)},
	issn = {2398-6352},
	url = {https://www.nature.com/articles/s41746-022-00742-2},
	doi = {10.1038/s41746-022-00742-2},
	language = {en},
	number = {1},
	urldate = {2025-02-26},
	journal = {npj Digital Medicine},
	author = {Yang, Xi and Chen, Aokun and PourNejatian, Nima and Shin, Hoo Chang and Smith, Kaleb E. and Parisien, Christopher and Compas, Colin and Martin, Cheryl and Costa, Anthony B. and Flores, Mona G. and Zhang, Ying and Magoc, Tanja and Harle, Christopher A. and Lipori, Gloria and Mitchell, Duane A. and Hogan, William R. and Shenkman, Elizabeth A. and Bian, Jiang and Wu, Yonghui},
	month = dec,
	year = {2022},
	note = {Publisher: Nature Publishing Group},
	keywords = {Health care, Medical research},
	pages = {1--9},
	file = {Full Text PDF:/Users/seowung/Zotero/storage/KJN4Q2I6/Yang 등 - 2022 - A large language model for electronic health recor.pdf:application/pdf},
}


@article{zhou_automorph_2022,
	title = {{AutoMorph}: {Automated} {Retinal} {Vascular} {Morphology} {Quantification} {Via} a {Deep} {Learning} {Pipeline}},
	volume = {11},
	issn = {2164-2591},
	shorttitle = {{AutoMorph}},
	url = {https://doi.org/10.1167/tvst.11.7.12},
	doi = {10.1167/tvst.11.7.12},
	number = {7},
	urldate = {2025-02-27},
	journal = {Translational Vision Science \& Technology},
	author = {Zhou, Yukun and Wagner, Siegfried K. and Chia, Mark A. and Zhao, An and Woodward-Court, Peter and Xu, Moucheng and Struyven, Robbert and Alexander, Daniel C. and Keane, Pearse A.},
	month = jul,
	year = {2022},
	pages = {12},
}

@article{sudlow_uk_2015,
	title = {{UK} {Biobank}: {An} {Open} {Access} {Resource} for {Identifying} the {Causes} of a {Wide} {Range} of {Complex} {Diseases} of {Middle} and {Old} {Age}},
	volume = {12},
	issn = {1549-1277},
	shorttitle = {{UK} {Biobank}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380465/},
	doi = {10.1371/journal.pmed.1001779},
	abstract = {Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.},
	number = {3},
	urldate = {2025-02-27},
	journal = {PLoS Medicine},
	author = {Sudlow, Cathie and Gallacher, John and Allen, Naomi and Beral, Valerie and Burton, Paul and Danesh, John and Downey, Paul and Elliott, Paul and Green, Jane and Landray, Martin and Liu, Bette and Matthews, Paul and Ong, Giok and Pell, Jill and Silman, Alan and Young, Alan and Sprosen, Tim and Peakman, Tim and Collins, Rory},
	month = mar,
	year = {2015},
	pmid = {25826379},
	pmcid = {PMC4380465},
	pages = {e1001779},
	file = {PubMed Central Full Text PDF:/Users/seowung/Zotero/storage/X23Z632M/Sudlow 등 - 2015 - UK Biobank An Open Access Resource for Identifyin.pdf:application/pdf},
}

@article{koh_relationship_2010,
	title = {Relationship of {Retinal} {Vascular} {Tortuosity} with the {Neuroretinal} {Rim}: {The} {Singapore} {Malay} {Eye} {Study}},
	volume = {51},
	issn = {1552-5783},
	shorttitle = {Relationship of {Retinal} {Vascular} {Tortuosity} with the {Neuroretinal} {Rim}},
	url = {https://doi.org/10.1167/iovs.09-5008},
	doi = {10.1167/iovs.09-5008},
	abstract = {To describe the association of retinal vascular tortuosity, measured quantitatively, with the neuroretinal rim.    A population-based, cross-sectional study was conducted in Malay persons aged 40 to 80 years residing in Singapore. Retinal vascular tortuosity was quantified by using a semiautomated, computer-assisted program assessing retinal fundus photographs, according to a standardized protocol. Optic disc measurements including disc area, rim area, and rim-to-disc area (RDA) ratio were obtained with a confocal scanning laser ophthalmoscope.    In analyses adjusting for age, sex, spherical equivalent, and retinal vascular caliber, reduced arteriolar and venular tortuosity were associated with a decrease in global RDA ratio (P = 0.006 and P = 0.001, respectively). When compared with the arterioles, retinal venular tortuosity demonstrated a stronger association with RDA ratio. The temporal-inferior region of the neuroretinal rim was most strongly associated with retinal vascular tortuosity.    Straighter retinal vessels were significantly associated with a thinning neuroretinal rim. These findings may provide additional insights into the geometric retinal vascular changes seen in early glaucomatous optic neuropathy.},
	number = {7},
	urldate = {2025-02-27},
	journal = {Investigative Ophthalmology \& Visual Science},
	author = {Koh, Victor and Cheung, Carol Yim-lui and Zheng, Yingfeng and Wong, Tien Yin and Wong, Wanling and Aung, Tin},
	month = jul,
	year = {2010},
	pages = {3736--3741},
	file = {Snapshot:/Users/seowung/Zotero/storage/K7GXQT25/article.html:text/html},
}

@article{durazzo_smoking_2014,
	title = {Smoking and increased {Alzheimer}’s disease risk: {A} review of potential mechanisms},
	volume = {10},
	issn = {1552-5260},
	shorttitle = {Smoking and increased {Alzheimer}’s disease risk},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4098701/},
	doi = {10.1016/j.jalz.2014.04.009},
	abstract = {Background
Cigarette smoking has been linked with both increased and decreased risk for Alzheimer’s disease (AD). This is relevant for the US military because the prevalence of smoking in the military is approximately 11\% higher than in civilians.

Methods
Systematic review of published studies on the association between smoking and increased risk for AD, and preclinical and human literature on the relationships between smoking, nicotine exposure and AD-related neuropathology. Original data from comparisons of smoking and never-smoking cognitively normal elders on in vivo amyloid imaging are also presented.

Results
Overall, the literature indicates that former/active smoking is related to a significantly increased risk for AD. Cigarette smoke/smoking is associated with AD neuropathology in preclinical models and humans. Smoking-related cerebral oxidative stress is a potential mechanism promoting AD pathophysiology and increased risk for AD.

Conclusions
A reduction in the incidence of smoking will likely reduce the future prevalence of AD.},
	number = {3 0},
	urldate = {2025-02-27},
	journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
	author = {Durazzo, Timothy C. and Mattsson, Niklas and Weiner, Michael W.},
	month = jun,
	year = {2014},
	pmid = {24924665},
	pmcid = {PMC4098701},
	pages = {S122--S145},
	file = {PubMed Central Full Text PDF:/Users/seowung/Zotero/storage/WCX3QY7H/Durazzo 등 - 2014 - Smoking and increased Alzheimer’s disease risk A .pdf:application/pdf},
}

@book{leshner_preventing_2017,
	address = {Washington, D.C.},
	title = {Preventing {Cognitive} {Decline} and {Dementia}: {A} {Way} {Forward}},
	isbn = {978-0-309-45959-4},
	shorttitle = {Preventing {Cognitive} {Decline} and {Dementia}},
	url = {https://www.nap.edu/catalog/24782},
	urldate = {2025-02-27},
	publisher = {National Academies Press},
	editor = {Leshner, Alan I. and Landis, Story and Stroud, Clare and Downey, Autumn},
	collaborator = {{Committee on Preventing Dementia and Cognitive Impairment} and {Board on Health Sciences Policy} and {Health and Medicine Division} and {National Academies of Sciences, Engineering, and Medicine}},
	month = sep,
	year = {2017},
	doi = {10.17226/24782},
	keywords = {Health and Medicine--Aging, Health and Medicine--Public Health and Prevention},
}

@misc{grattafiori_llama_2024,
	title = {The {Llama} 3 {Herd} of {Models}},
	url = {http://arxiv.org/abs/2407.21783},
	doi = {10.48550/arXiv.2407.21783},
	abstract = {Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.},
	urldate = {2025-02-27},
	publisher = {arXiv},
	author = {Grattafiori, Aaron and Dubey, Abhimanyu and Jauhri, Abhinav and others},
	month = nov,
	year = {2024},
	note = {arXiv:2407.21783 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition},
	file = {Preprint PDF:/Users/seowung/Zotero/storage/MKTS46YZ/Grattafiori 등 - 2024 - The Llama 3 Herd of Models.pdf:application/pdf;Snapshot:/Users/seowung/Zotero/storage/92EBHM8B/2407.html:text/html},
}

@misc{radford_learning_2021,
	title = {Learning {Transferable} {Visual} {Models} {From} {Natural} {Language} {Supervision}},
	url = {http://arxiv.org/abs/2103.00020},
	doi = {10.48550/arXiv.2103.00020},
	abstract = {State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.},
	urldate = {2025-02-27},
	publisher = {arXiv},
	author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
	month = feb,
	year = {2021},
	note = {arXiv:2103.00020 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {Preprint PDF:/Users/seowung/Zotero/storage/FMVX2GUV/Radford 등 - 2021 - Learning Transferable Visual Models From Natural L.pdf:application/pdf;Snapshot:/Users/seowung/Zotero/storage/R3JDX4R6/2103.html:text/html},
}

@article{cheung_microvascular_2014,
	title = {Microvascular network alterations in the retina of patients with {Alzheimer}'s disease},
	volume = {10},
	issn = {1552-5279},
	doi = {10.1016/j.jalz.2013.06.009},
	abstract = {BACKGROUND: Although cerebral small-vessel disease has been implicated in the development of Alzheimer's disease (AD), the cerebral microcirculation is difficult to visualize directly in vivo. Because the retina provides a noninvasive window to assess the microcirculation, we determined whether quantitatively measured retinal microvascular parameters are associated with AD.
METHODS: We conducted a case-control study (case:control matching ≈ 1:2). Retinal photographs were analyzed using a computer program, and a spectrum of quantitative retinal microvascular parameters (caliber, fractal dimension, tortuosity, and bifurcation) were measured. Logistic regression models were used to compute the odds ratio (OR) and 95\% confidence interval for AD adjusting for age, gender, ethnicity, smoking, hypertension, diabetes, hypercholesterolemia, and history of myocardial infarction.
RESULTS: We included 136 demented patients with AD and 290 age-gender-race-matched controls. Persons with narrower venular caliber (OR per standard deviation [SD] decrease, 2.01 [1.27-3.19]), decreased arteriolar and venular fractal dimension (OR per SD decrease 1.35 [1.08-1.68], 1.47 [1.17-1.84], respectively) and increased arteriolar and venular tortuosity (OR per SD increase, 1.84 [1.40-2.31], 1.94 [1.48-2.53], respectively) were more likely to have AD. These associations still persisted when only AD cases without a history of cerebrovascular disease were included.
CONCLUSIONS: Patients with AD have altered microvascular network in the retina (narrower retinal venules and a sparser and more tortuous retinal vessels) compared with matched nondemented controls. These changes in retinal microvasculature may reflect similar pathophysiological processes in cerebral microvasculature in the brains of patients with AD.},
	language = {eng},
	number = {2},
	journal = {Alzheimer's \& Dementia: The Journal of the Alzheimer's Association},
	author = {Cheung, Carol Yim-Lui and Ong, Yi Ting and Ikram, M. Kamran and Ong, Shin Yeu and Li, Xiang and Hilal, Saima and Catindig, Joseree-Ann S. and Venketasubramanian, Narayanaswamy and Yap, Philip and Seow, Dennis and Chen, Christopher P. and Wong, Tien Yin},
	month = mar,
	year = {2014},
	pmid = {24439169},
	keywords = {Aged, Alzheimer Disease, Alzheimer's disease, Case-Control Studies, Female, Humans, Male, Microcirculation, Microvessels, Retina, Retinal vasculature, Retinal Vessels, Retinoscopy, Retrospective Studies, Small-vessel disease},
	pages = {135--142},
}

@inproceedings{bulat2024fff,
  title={{FFF}: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models},
  author={Bulat, Adrian and Ouali, Yassine and Tzimiropoulos, Georgios},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14172--14182},
  year={2024}
}

@ARTICLE{Litke2021-bh,
  title     = "Modifiable risk factors in Alzheimer disease and related
               dementias: A review",
  author    = "Litke, Rachel and Garcharna, Lorena Cancino and Jiwani, Salima
               and Neugroschl, Judith",
  abstract  = "PURPOSE: Although Alzheimer disease and related dementias
               (ADRDs) have long been considered nonpreventable and even an
               inevitable consequence of aging, recent findings from
               longitudinal studies indicate a downtrend in age-adjusted
               incidence and prevalence of ADRDs in Western countries. This
               remarkable trend might be the result of improved management of
               so-called modifiable risk factors. The aim of this review is to
               present evidence of modifiable factors of ADRDs in a life-course
               approach. METHODS: A PubMed database search was conducted
               between November and December 2020 to identify relevant studies
               evaluating the role of modifiable risk factors in the
               development of ADRDs. Key words (Alzheimer's disease and
               modifiable risk factors) were used and specific inclusion and
               exclusion criteria applied. FINDINGS: This review identifies
               modifiable factors for ADRDs divided into early-life,
               middle-life, and late-life risk factors, depending on the
               available window of preventive action. According to life course
               exposure, factors can be protective or deleterious for ADRDs
               that participate in the underlying pathophysiologic complexity
               of these diseases as well as the complexity for public health
               measures implementations. IMPLICATIONS: The available evidence
               derived from epidemiologic, preclinical, interventional studies
               suggest that modifiable risk factors for ADRDs offer
               opportunities for therapeutic and preventive actions.",
  journal   = "Clin. Ther.",
  publisher = "Elsevier BV",
  volume    =  43,
  number    =  6,
  pages     = "953--965",
  month     =  jun,
  year      =  2021,
  keywords  = "Alzheimer disease and related dementias; aging; environmental
               factors; metabolic syndrome; prevention; vascular disease",
  language  = "en"
}

@article{livingston_dementia_2024,
	title = {Dementia prevention, intervention, and care: 2024 report of the {Lancet} standing {Commission}},
	volume = {404},
	issn = {0140-6736},
	url = {https://doi.org/10.1016/S0140-6736(24)01296-0},
	doi = {10.1016/S0140-6736(24)01296-0},
	number = {10452},
	urldate = {2025-11-06},
	journal = {The Lancet},
	author = {Livingston, Gill and Huntley, Jonathan and Liu, Kathy Y and Costafreda, Sergi G and Selbæk, Geir and Alladi, Suvarna and Ames, David and Banerjee, Sube and Burns, Alistair and Brayne, Carol and Fox, Nick C and Ferri, Cleusa P and Gitlin, Laura N and Howard, Robert and Kales, Helen C and Kivimäki, Mika and Larson, Eric B and Nakasujja, Noeline and Rockwood, Kenneth and Samus, Quincy and Shirai, Kokoro and Singh-Manoux, Archana and Schneider, Lon S and Walsh, Sebastian and Yao, Yao and Sommerlad, Andrew and Mukadam, Naaheed},
	month = aug,
	year = {2024},
	note = {Publisher: Elsevier},
	pages = {572--628},
	annote = {doi: 10.1016/S0140-6736(24)01296-0},
}


@article{hayden_association_2024,
	title = {Association between {Modifiable} {Risk} {Factors} and {Levels} of {Blood}-{Based} {Biomarkers} of {Alzheimer}’s and {Related} {Dementias} in the {Look} {AHEAD} {Cohort}},
	volume = {13},
	issn = {2534-773X},
	url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC10775955/},
	doi = {10.14283/jarlife.2024.1},
	abstract = {Background
Emerging evidence suggests that a number of factors can influence blood-based biomarker levels for Alzheimer’s disease (AD) and Alzheimer’s related dementias (ADRD). We examined the associations that demographic and clinical characteristics have with AD/ADRD blood-based biomarker levels in an observational continuation of a clinical trial cohort of older individuals with type 2 diabetes and overweight or obesity.

Methods
Participants aged 45-76 years were randomized to a 10-year Intensive Lifestyle Intervention (ILI) or a diabetes support and education (DSE) condition. Stored baseline and end of intervention (8-13 years later) plasma samples were analyzed with the Quanterix Simoa HD-X Analyzer. Changes in Aβ42, Aβ40, Aβ42/Aβ40, ptau181, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) were evaluated in relation to randomization status, demographic, and clinical characteristics.

Results
In a sample of 779 participants from the Look AHEAD cohort, we found significant associations between blood-based biomarkers for AD/ADRD and 15 of 18 demographic (age, gender, race and ethnicity, education) and clinical characteristics (APOE, depression, alcohol use, smoking, body mass index, HbA1c, diabetes duration, diabetes treatment, estimated glomerular filtration rate, hypertension, and history of cardiovascular disease) .

Conclusions
Blood-based biomarkers of AD/ADRD are influenced by common demographic and clinical characteristics. These factors should be considered carefully when interpreting these AD/ADRD blood biomarker values for clinical or research purposes.},
	urldate = {2025-10-30},
	journal = {JAR life},
	author = {Hayden, K.M. and Mielke, M.M. and Evans, J.K. and Neiberg, R. and Molina-Henry, D. and Culkin, M. and Marcovina, S. and Johnson, K.C. and Carmichael, O.T. and Rapp, S.R. and Sachs, B.C. and Ding, J. and Shappell, H. and Wagenknecht, L. and Luchsinger, J.A. and Espeland, M.A.},
	month = jan,
	year = {2024},
	pmid = {38204926},
	pmcid = {PMC10775955},
	pages = {1--21},
	file = {Full Text PDF:/Users/leem.s/Zotero/storage/TA7EWHLG/Hayden 등 - Association between Modifiable Risk Factors and Levels of Blood-Based Biomarkers of Alzheimer’s and.pdf:application/pdf},
}

@article{huszar_association_2024,
	title = {Association of modifiable risk factors with progression to dementia in relation to amyloid and tau pathology},
	volume = {16},
	issn = {1758-9193},
	url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC11515263/},
	doi = {10.1186/s13195-024-01602-9},
	abstract = {Background
Dementia preventive interventions targeting multiple modifiable risk factors are a promising approach. However, the impact of modifiable risk factors in the presence of beta-amyloid or phosphorylated-tau (p-tau) pathology is unclear.

Methods
The objective of the study was to examine the role of modifiable risk factors (vascular factors, depression, and smoking) in the progression to mild cognitive impairment (MCI) or dementia among 434 cognitively unimpaired (CU) and 611 individuals with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Vascular risk factors were summarized with the Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) score, dichotomized into higher versus lower risk. Depression and smoking (yes/no) were categorised according to medical history or current symptoms. Analyses were stratified by beta-amyloid negative (A-) and positive (A +), p-tau negative (T-) and positive (T +), or beta-amyloid and p-tau negative (A-T-) and positive (A + T +) biomarker status. Cox proportional hazard models were adjusted for age, sex, education, baseline MMSE score, baseline hippocampal volume and ApoE4 carrier status.

Results
Higher CAIDE score was associated with increased risk of progression to all-cause dementia in most MCI subgroups: adjusted hazard ratios (aHR) [95\% CI] were 3.1 [1.43; 6.53] in the A- subgroup, 1.7 [1.20–2.27] in T + , 2.6 [1.06–6.59] in A-T-, and 1.6 [1.15–2.22] in the A + T + subgroup. Smoking (yes/no) was associated with increased dementia aHR in the A + MCI subgroup: 1.6 [1.07–2.34]. Depression increased dementia aHR in the T + MCI subgroup: 1.5 [1.06–2.02]. No significant associations were found in the CU biomarker subgroups.

Conclusion
Addressing modifiable risk factors carries an important potential for reducing the risk of dementia even after the onset of Alzheimer's pathology. Knowledge of biomarker status can further optimize prevention strategies.

Supplementary Information
The online version contains supplementary material available at 10.1186/s13195-024-01602-9.},
	urldate = {2025-10-31},
	journal = {Alzheimer's Research \& Therapy},
	author = {Huszár, Zsolt and Solomon, Alina and Engh, Marie Anne and Koszovácz, Vanda and Terebessy, Tamás and Molnár, Zsolt and Hegyi, Péter and Horváth, András and Mangialasche, Francesca and Kivipelto, Miia and Csukly, Gábor},
	month = oct,
	year = {2024},
	pmid = {39462394},
	pmcid = {PMC11515263},
	pages = {238},
	file = {Full Text PDF:/Users/leem.s/Zotero/storage/XNKIE67P/Huszár 등 - 2024 - Association of modifiable risk factors with progression to dementia in relation to amyloid and tau p.pdf:application/pdf},
}

@article{xiong_review_2023,
	title = {Review of {Risk} {Factors} {Associated} {With} {Biomarkers} for {Alzheimer} {Disease}},
	volume = {55},
	issn = {1945-2810},
	doi = {10.1097/JNN.0000000000000705},
	abstract = {BACKGROUND: Alzheimer disease (AD) is a neurodegenerative disease with no cure. The number of individuals living with AD doubles every 5 years. The current clinical practice relies on clinical history, mental status tests, cerebrum imaging, and physical and neurological examinations; however, recent advances in the field of biomarkers have provided clues for the early detection of AD. High levels of tau and low levels of amyloid-β (Aβ) in cerebrospinal fluid are well-known biomarkers for AD. METHODS: A database search of PubMed, Ovid MEDLINE, and CINAHL was conducted to identify relevant articles published within the last 5 years. The search was limited to articles concerning adults 65 years or older and published in the English language. Twelve articles were included in the review. RESULTS: Risk factors of sleep disruption, depression, and motor function are implicated. Cerebrospinal fluid parameters for biomarkers of tau and Aβ were universally lower among Blacks compared with Whites, raising concern that norm reference may not be accurate for all populations. Older adults are more at risk for AD. Results are inconclusive regarding whether depression is related to Aβ and tau pathology. CONCLUSION: Nurses should screen for sleep architecture, depression, and motor function in their patients and educate them on good sleep hygiene. Sleep studies should be advocated for people with suspected sleep apnea to mitigate the risk factor related to abnormal Aβ and tau pathology. Falls and decreased motor function require screening because they may be early indicators of abnormal biomarkers leading to AD.},
	language = {eng},
	number = {3},
	journal = {The Journal of Neuroscience Nursing: Journal of the American Association of Neuroscience Nurses},
	author = {Xiong, Jiayue and Bhimani, Rozina and Carney-Anderson, Lisa},
	month = jun,
	year = {2023},
	pmid = {37094378},
	keywords = {Aged, Alzheimer Disease, Amyloid beta-Peptides, Biomarkers, Humans, Neurodegenerative Diseases, Risk Factors, tau Proteins},
	pages = {103--109},
}

@article{sprecher_poor_2017,
	title = {Poor sleep is associated with {CSF} biomarkers of amyloid pathology in cognitively normal adults},
	volume = {89},
	issn = {0028-3878},
	url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC5539733/},
	doi = {10.1212/WNL.0000000000004171},
	abstract = {Objective:
To determine the relationship between sleep quality and CSF markers of Alzheimer disease (AD) pathology in late midlife.

Methods:
We investigated the relationship between sleep quality and CSF AD biomarkers in a cohort enriched for parental history of sporadic AD, the Wisconsin Registry for Alzheimer's Prevention. A total of 101 participants (mean age 62.9 ± 6.2 years, 65.3\% female) completed sleep assessments and CSF collection and were cognitively normal. Sleep quality was measured with the Medical Outcomes Study Sleep Scale. CSF was assayed for biomarkers of amyloid metabolism and plaques (β-amyloid 42 [Aβ42]), tau pathology (phosphorylated tau [p-tau] 181), neuronal/axonal degeneration (total tau [t-tau], neurofilament light [NFL]), neuroinflammation/astroglial activation (monocyte chemoattractant protein–1 [MCP-1], chitinase-3-like protein 1 [YKL-40]), and synaptic dysfunction/degeneration (neurogranin). To adjust for individual differences in total amyloid production, Aβ42 was expressed relative to Aβ40. To assess cumulative pathology, CSF biomarkers were expressed in ratio to Aβ42. Relationships among sleep scores and CSF biomarkers were assessed with multiple regression, controlling for age, sex, time between sleep and CSF measurements, and CSF assay batch.

Results:
Worse subjective sleep quality, more sleep problems, and daytime somnolence were associated with greater AD pathology, indicated by lower CSF Aβ42/Aβ40 and higher t-tau/Aβ42, p-tau/Aβ42, MCP-1/Aβ42, and YKL-40/Aβ42. There were no significant associations between sleep and NFL or neurogranin.

Conclusions:
Self-report of poor sleep was associated with greater AD-related pathology in cognitively healthy adults at risk for AD. Effective strategies exist for improving sleep; therefore sleep health may be a tractable target for early intervention to attenuate AD pathogenesis.},
	number = {5},
	urldate = {2025-10-31},
	journal = {Neurology},
	author = {Sprecher, Kate E. and Koscik, Rebecca L. and Carlsson, Cynthia M. and Zetterberg, Henrik and Blennow, Kaj and Okonkwo, Ozioma C. and Sager, Mark A. and Asthana, Sanjay and Johnson, Sterling C. and Benca, Ruth M. and Bendlin, Barbara B.},
	month = aug,
	year = {2017},
	pmid = {28679595},
	pmcid = {PMC5539733},
	pages = {445--453},
	file = {Full Text PDF:/Users/leem.s/Zotero/storage/W2QPDBCN/Sprecher 등 - 2017 - Poor sleep is associated with CSF biomarkers of amyloid pathology in cognitively normal adults.pdf:application/pdf},
}

@ARTICLE{Gagnier2013-bh,
  title     = "The {CARE} guidelines: Consensus-based clinical case reporting
               guideline development",
  author    = "Gagnier, Joel J and Kienle, Gunver and Altman, Douglas G and
               Moher, David and Sox, Harold and Riley, David and {CARE Group*}",
  abstract  = "BACKGROUND: A case report is a narrative that describes, for
               medical, scientific, or educational purposes, a medical problem
               experienced by one or more patients. Case reports written
               without guidance from reporting standards are insufficiently
               rigorous to guide clinical practice or to inform clinical study
               design. PRIMARY OBJECTIVE: Develop, disseminate, and implement
               systematic reporting guidelines for case reports. METHODS: We
               used a three-phase consensus process consisting of (1)
               premeeting literature review and interviews to generate items
               for the reporting guidelines, (2) a face-to-face consensus
               meeting to draft the reporting guidelines, and (3) postmeeting
               feedback, review, and pilot testing, followed by finalization of
               the case report guidelines. RESULTS: This consensus process
               involved 27 participants and resulted in a 13-item checklist-a
               reporting guideline for case reports. The primary items of the
               checklist are title, key words, abstract, introduction, patient
               information, clinical findings, timeline, diagnostic assessment,
               therapeutic interventions, follow-up and outcomes, discussion,
               patient perspective, and informed consent. CONCLUSIONS: We
               believe the implementation of the CARE (CAse REport) guidelines
               by medical journals will improve the completeness and
               transparency of published case reports and that the systematic
               aggregation of information from case reports will inform
               clinical study design, provide early signals of effectiveness
               and harms, and improve healthcare delivery.",
  journal   = "Glob. Adv. Health Med.",
  publisher = "SAGE Publications",
  volume    =  2,
  number    =  5,
  pages     = "38--43",
  month     =  sep,
  year      =  2013,
  keywords  = "Case report; EQUATOR Network; case study; health research
               reporting guidelines; meaningful use; patient reports",
  language  = "en"
}

@INPROCEEDINGS{Akiba2019-uw,
  title           = "Optuna",
  booktitle       = "Proceedings of the 25th {ACM} {SIGKDD} International
                     Conference on Knowledge Discovery \& Data Mining",
  author          = "Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and
                     Ohta, Takeru and Koyama, Masanori",
  publisher       = "ACM",
  pages           = "2623--2631",
  month           =  jul,
  year            =  2019,
  address         = "New York, NY, USA",
  copyright       = "https://www.acm.org/publications/policies/copyright\_policy\#Background",
  conference      = "KDD '19: The 25th ACM SIGKDD Conference on Knowledge
                     Discovery and Data Mining",
  location        = "Anchorage AK USA"
}

@ARTICLE{Xiaopeng2025-tc,
  title    = "Global Burden of Alzheimer's disease and other dementias in
              adults aged 65 years and older, 1991-2021: population-based study",
  author   = "Xiaopeng, Zhu and Jing, Yu and Xia, Lai and Xingsheng, Wang and
              Juan, Deng and Yan, Long and Baoshan, Li",
  abstract = "Objectives: This study aims to analyze the prevalence, impact,
              and disparities of Alzheimer's disease and other dementias (ADRD)
              among adults aged 65 years and older worldwide, across different
              regions and countries, spanning the years 1991-2021. Methods: GBD
              2021 obtained data on dementia from vital registration systems,
              published scientific literature and surveys, and data from
              health-service encounters on deaths, excess mortality,
              prevalence, and incidence from 1991 to 2021, through systematic
              review and additional data-seeking efforts. Individuals aged
              $\geq$65 years from 21 regions and 204 countries and territories
              (Global Burden of Disease and Risk Factors Study 2021) from 1991
              to 2021 were analyzed. Primary outcomes were ADRD related to aged
              $\geq$65 years age-standardized prevalence, deaths,
              disability-adjusted life years (DALYs), and average annual
              percentage change (AAPC), and the fractions of these metrics that
              were attributable to three risk factors (high BMI, high fasting
              glucose, and smoking) that met GBD. Results: The global mortality
              of ADRD among adults aged $\geq$65 years increased by 115\%, from
              6.5 (95\% UI 1.5-18) per 100,000 population in 1991 to 14 (95\%
              UI 3.5-37) per 100,000 population in 2021, with an Average Annual
              Percentage Change (AAPC) of 1.10\% (95\%CI 0.45\%-1.76\%). The
              prevalence of ADRD in adults aged $\geq$65 years increased by
              160\% between 1991 and 2021, from 18.7 (95\%UI 14.9-23.2) million
              to 49 (95\%UI 38.6-61.2) million. The aged $\geq$65 years
              age-standardized prevalence of ADRD in this age group increased
              from 11,977 (95\%UI 9,438-14,935) per 100,000 population in 1991
              to 12,124 (95\%UI 9,489-15,204) per 100,000 population in 2021.
              The aged $\geq$65 years the number of prevalent persons was more
              significant among females than among males (males: from 6.2
              (95\%UI 4.8-7.8) million in 1991 to 17.2 (95\%UI 13.4-21.6)
              million in 2021; women: from 12.5 (95\%UI 10.0-15.4) million in
              1991 to 31.7 (95\%UI 25.1-39.6) million in 2021). In 2021, ADRD
              in adults aged $\geq$65 years caused 8.02 (95\%UI 1.34-22.19)
              million deaths and 25.38 (95\%UI 23.18-71.20) million DALYs
              attributable to dementia, and high BMI, high fasting glucose, and
              smoking remained modifiable risk factors in all risk factors.
              Conclusions: From 1991 to 2021, the worldwide prevalence of
              Alzheimer's disease and other dementias among adults aged 65 and
              above has increased by 1.6 times, largely driven by the expanding
              older adults. This escalating trend poses significant challenges
              to the global healthcare system in terms of both mortality rates
              and disability-adjusted life years. We recommend that
              standardized screening programmes be promoted globally,
              especially in high-risk areas and among high-risk populations,
              for early detection and intervention of the disease. Secondly, we
              recommend strengthening the management of risk factor elements.",
  journal  = "Front. Public Health",
  volume   =  13,
  pages    = "1585711",
  month    =  jul,
  year     =  2025,
  keywords = "65 years and older; Alzheimer's disease and other dementias; GBD;
              Global Burden; old age",
  language = "en"
}

@misc{grattafiori2024llama3herdmodels,
      title={The Llama 3 Herd of Models}, 
      author={Aaron Grattafiori and Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Alex Vaughan and Amy Yang and Angela Fan and Anirudh Goyal and Anthony Hartshorn and Aobo Yang and Archi Mitra and Archie Sravankumar and Artem Korenev and Arthur Hinsvark and Arun Rao and Aston Zhang and Aurelien Rodriguez and Austen Gregerson and Ava Spataru and Baptiste Roziere and Bethany Biron and Binh Tang and Bobbie Chern and Charlotte Caucheteux and Chaya Nayak and Chloe Bi and Chris Marra and Chris McConnell and Christian Keller and Christophe Touret and Chunyang Wu and Corinne Wong and Cristian Canton Ferrer and Cyrus Nikolaidis and Damien Allonsius and Daniel Song and Danielle Pintz and Danny Livshits and Danny Wyatt and David Esiobu and Dhruv Choudhary and Dhruv Mahajan and Diego Garcia-Olano and Diego Perino and Dieuwke Hupkes and Egor Lakomkin and Ehab AlBadawy and Elina Lobanova and Emily Dinan and Eric Michael Smith and Filip Radenovic and Francisco Guzmán and Frank Zhang and Gabriel Synnaeve and Gabrielle Lee and Georgia Lewis Anderson and Govind Thattai and Graeme Nail and Gregoire Mialon and Guan Pang and Guillem Cucurell and Hailey Nguyen and Hannah Korevaar and Hu Xu and Hugo Touvron and Iliyan Zarov and Imanol Arrieta Ibarra and Isabel Kloumann and Ishan Misra and Ivan Evtimov and Jack Zhang and Jade Copet and Jaewon Lee and Jan Geffert and Jana Vranes and Jason Park and Jay Mahadeokar and Jeet Shah and Jelmer van der Linde and Jennifer Billock and Jenny Hong and Jenya Lee and Jeremy Fu and Jianfeng Chi and Jianyu Huang and Jiawen Liu and Jie Wang and Jiecao Yu and Joanna Bitton and Joe Spisak and Jongsoo Park and Joseph Rocca and Joshua Johnstun and Joshua Saxe and Junteng Jia and Kalyan Vasuden Alwala and Karthik Prasad and Kartikeya Upasani and Kate Plawiak and Ke Li and Kenneth Heafield and Kevin Stone and Khalid El-Arini and Krithika Iyer and Kshitiz Malik and Kuenley Chiu and Kunal Bhalla and Kushal Lakhotia and Lauren Rantala-Yeary and Laurens van der Maaten and Lawrence Chen and Liang Tan and Liz Jenkins and Louis Martin and Lovish Madaan and Lubo Malo and Lukas Blecher and Lukas Landzaat and Luke de Oliveira and Madeline Muzzi and Mahesh Pasupuleti and Mannat Singh and Manohar Paluri and Marcin Kardas and Maria Tsimpoukelli and Mathew Oldham and Mathieu Rita and Maya Pavlova and Melanie Kambadur and Mike Lewis and Min Si and Mitesh Kumar Singh and Mona Hassan and Naman Goyal and Narjes Torabi and Nikolay Bashlykov and Nikolay Bogoychev and Niladri Chatterji and Ning Zhang and Olivier Duchenne and Onur Çelebi and Patrick Alrassy and Pengchuan Zhang and Pengwei Li and Petar Vasic and Peter Weng and Prajjwal Bhargava and Pratik Dubal and Praveen Krishnan and Punit Singh Koura and Puxin Xu and Qing He and Qingxiao Dong and Ragavan Srinivasan and Raj Ganapathy and Ramon Calderer and Ricardo Silveira Cabral and Robert Stojnic and Roberta Raileanu and Rohan Maheswari and Rohit Girdhar and Rohit Patel and Romain Sauvestre and Ronnie Polidoro and Roshan Sumbaly and Ross Taylor and Ruan Silva and Rui Hou and Rui Wang and Saghar Hosseini and Sahana Chennabasappa and Sanjay Singh and Sean Bell and Seohyun Sonia Kim and Sergey Edunov and Shaoliang Nie and Sharan Narang and Sharath Raparthy and Sheng Shen and Shengye Wan and Shruti Bhosale and Shun Zhang and Simon Vandenhende and Soumya Batra and Spencer Whitman and Sten Sootla and Stephane Collot and Suchin Gururangan and Sydney Borodinsky and Tamar Herman and Tara Fowler and Tarek Sheasha and Thomas Georgiou and Thomas Scialom and Tobias Speckbacher and Todor Mihaylov and Tong Xiao and Ujjwal Karn and Vedanuj Goswami and Vibhor Gupta and Vignesh Ramanathan and Viktor Kerkez and Vincent Gonguet and Virginie Do and Vish Vogeti and Vítor Albiero and Vladan Petrovic and Weiwei Chu and Wenhan Xiong and Wenyin Fu and Whitney Meers and Xavier Martinet and Xiaodong Wang and Xiaofang Wang and Xiaoqing Ellen Tan and Xide Xia and Xinfeng Xie and Xuchao Jia and Xuewei Wang and Yaelle Goldschlag and Yashesh Gaur and Yasmine Babaei and Yi Wen and Yiwen Song and Yuchen Zhang and Yue Li and Yuning Mao and Zacharie Delpierre Coudert and Zheng Yan and Zhengxing Chen and Zoe Papakipos and Aaditya Singh and Aayushi Srivastava and Abha Jain and Adam Kelsey and Adam Shajnfeld and Adithya Gangidi and Adolfo Victoria and Ahuva Goldstand and Ajay Menon and Ajay Sharma and Alex Boesenberg and Alexei Baevski and Allie Feinstein and Amanda Kallet and Amit Sangani and Amos Teo and Anam Yunus and Andrei Lupu and Andres Alvarado and Andrew Caples and Andrew Gu and Andrew Ho and Andrew Poulton and Andrew Ryan and Ankit Ramchandani and Annie Dong and Annie Franco and Anuj Goyal and Aparajita Saraf and Arkabandhu Chowdhury and Ashley Gabriel and Ashwin Bharambe and Assaf Eisenman and Azadeh Yazdan and Beau James and Ben Maurer and Benjamin Leonhardi and Bernie Huang and Beth Loyd and Beto De Paola and Bhargavi Paranjape and Bing Liu and Bo Wu and Boyu Ni and Braden Hancock and Bram Wasti and Brandon Spence and Brani Stojkovic and Brian Gamido and Britt Montalvo and Carl Parker and Carly Burton and Catalina Mejia and Ce Liu and Changhan Wang and Changkyu Kim and Chao Zhou and Chester Hu and Ching-Hsiang Chu and Chris Cai and Chris Tindal and Christoph Feichtenhofer and Cynthia Gao and Damon Civin and Dana Beaty and Daniel Kreymer and Daniel Li and David Adkins and David Xu and Davide Testuggine and Delia David and Devi Parikh and Diana Liskovich and Didem Foss and Dingkang Wang and Duc Le and Dustin Holland and Edward Dowling and Eissa Jamil and Elaine Montgomery and Eleonora Presani and Emily Hahn and Emily Wood and Eric-Tuan Le and Erik Brinkman and Esteban Arcaute and Evan Dunbar and Evan Smothers and Fei Sun and Felix Kreuk and Feng Tian and Filippos Kokkinos and Firat Ozgenel and Francesco Caggioni and Frank Kanayet and Frank Seide and Gabriela Medina Florez and Gabriella Schwarz and Gada Badeer and Georgia Swee and Gil Halpern and Grant Herman and Grigory Sizov and Guangyi and Zhang and Guna Lakshminarayanan and Hakan Inan and Hamid Shojanazeri and Han Zou and Hannah Wang and Hanwen Zha and Haroun Habeeb and Harrison Rudolph and Helen Suk and Henry Aspegren and Hunter Goldman and Hongyuan Zhan and Ibrahim Damlaj and Igor Molybog and Igor Tufanov and Ilias Leontiadis and Irina-Elena Veliche and Itai Gat and Jake Weissman and James Geboski and James Kohli and Janice Lam and Japhet Asher and Jean-Baptiste Gaya and Jeff Marcus and Jeff Tang and Jennifer Chan and Jenny Zhen and Jeremy Reizenstein and Jeremy Teboul and Jessica Zhong and Jian Jin and Jingyi Yang and Joe Cummings and Jon Carvill and Jon Shepard and Jonathan McPhie and Jonathan Torres and Josh Ginsburg and Junjie Wang and Kai Wu and Kam Hou U and Karan Saxena and Kartikay Khandelwal and Katayoun Zand and Kathy Matosich and Kaushik Veeraraghavan and Kelly Michelena and Keqian Li and Kiran Jagadeesh and Kun Huang and Kunal Chawla and Kyle Huang and Lailin Chen and Lakshya Garg and Lavender A and Leandro Silva and Lee Bell and Lei Zhang and Liangpeng Guo and Licheng Yu and Liron Moshkovich and Luca Wehrstedt and Madian Khabsa and Manav Avalani and Manish Bhatt and Martynas Mankus and Matan Hasson and Matthew Lennie and Matthias Reso and Maxim Groshev and Maxim Naumov and Maya Lathi and Meghan Keneally and Miao Liu and Michael L. Seltzer and Michal Valko and Michelle Restrepo and Mihir Patel and Mik Vyatskov and Mikayel Samvelyan and Mike Clark and Mike Macey and Mike Wang and Miquel Jubert Hermoso and Mo Metanat and Mohammad Rastegari and Munish Bansal and Nandhini Santhanam and Natascha Parks and Natasha White and Navyata Bawa and Nayan Singhal and Nick Egebo and Nicolas Usunier and Nikhil Mehta and Nikolay Pavlovich Laptev and Ning Dong and Norman Cheng and Oleg Chernoguz and Olivia Hart and Omkar Salpekar and Ozlem Kalinli and Parkin Kent and Parth Parekh and Paul Saab and Pavan Balaji and Pedro Rittner and Philip Bontrager and Pierre Roux and Piotr Dollar and Polina Zvyagina and Prashant Ratanchandani and Pritish Yuvraj and Qian Liang and Rachad Alao and Rachel Rodriguez and Rafi Ayub and Raghotham Murthy and Raghu Nayani and Rahul Mitra and Rangaprabhu Parthasarathy and Raymond Li and Rebekkah Hogan and Robin Battey and Rocky Wang and Russ Howes and Ruty Rinott and Sachin Mehta and Sachin Siby and Sai Jayesh Bondu and Samyak Datta and Sara Chugh and Sara Hunt and Sargun Dhillon and Sasha Sidorov and Satadru Pan and Saurabh Mahajan and Saurabh Verma and Seiji Yamamoto and Sharadh Ramaswamy and Shaun Lindsay and Shaun Lindsay and Sheng Feng and Shenghao Lin and Shengxin Cindy Zha and Shishir Patil and Shiva Shankar and Shuqiang Zhang and Shuqiang Zhang and Sinong Wang and Sneha Agarwal and Soji Sajuyigbe and Soumith Chintala and Stephanie Max and Stephen Chen and Steve Kehoe and Steve Satterfield and Sudarshan Govindaprasad and Sumit Gupta and Summer Deng and Sungmin Cho and Sunny Virk and Suraj Subramanian and Sy Choudhury and Sydney Goldman and Tal Remez and Tamar Glaser and Tamara Best and Thilo Koehler and Thomas Robinson and Tianhe Li and Tianjun Zhang and Tim Matthews and Timothy Chou and Tzook Shaked and Varun Vontimitta and Victoria Ajayi and Victoria Montanez and Vijai Mohan and Vinay Satish Kumar and Vishal Mangla and Vlad Ionescu and Vlad Poenaru and Vlad Tiberiu Mihailescu and Vladimir Ivanov and Wei Li and Wenchen Wang and Wenwen Jiang and Wes Bouaziz and Will Constable and Xiaocheng Tang and Xiaojian Wu and Xiaolan Wang and Xilun Wu and Xinbo Gao and Yaniv Kleinman and Yanjun Chen and Ye Hu and Ye Jia and Ye Qi and Yenda Li and Yilin Zhang and Ying Zhang and Yossi Adi and Youngjin Nam and Yu and Wang and Yu Zhao and Yuchen Hao and Yundi Qian and Yunlu Li and Yuzi He and Zach Rait and Zachary DeVito and Zef Rosnbrick and Zhaoduo Wen and Zhenyu Yang and Zhiwei Zhao and Zhiyu Ma},
      year={2024},
      eprint={2407.21783},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2407.21783}, 
}

@ARTICLE{Ravichandran2025-hv,
  title    = "Association and multimodal model of retinal and blood-based
              biomarkers for detection of preclinical Alzheimer's disease",
  author   = "Ravichandran, Swetha and Snyder, Peter J and Alber, Jessica and
              Murchison, Charles F and Chaby, Lauren E and Jeromin, Andreas and
              Arthur, Edmund",
  abstract = "BACKGROUND: The potential diagnostic value of plasma
              amyloidogenic beta residue 42/40 ratio (A$\beta$42/A$\beta$40
              ratio), neurofilament light (NfL), tau phosphorylated at
              threonine-181 (p-tau181), and threonine-217 (p-tau217) has been
              extensively discussed in the literature. We have also previously
              described the association between retinal biomarkers and
              preclinical Alzheimer's disease (AD). The goal of this study was
              to evaluate the association, and a multimodal model of, retinal
              and plasma biomarkers for detection of preclinical AD. METHODS:
              We included 82 cognitively unimpaired (CU) participants (141
              eyes; mean age: 67 years; range: 56-80) from the Atlas of Retinal
              Imaging in Alzheimer's Study (ARIAS). Blood samples were assessed
              for concentrations of A$\beta$42/A$\beta$40 ratio, NfL, p-tau181,
              and p-tau217 (ALZpath, Inc.) using Single molecule array (SIMOA)
              technology. The Spectralis II system (Heidelberg Engineering) was
              used to acquire macular centered Spectral Domain Optical
              Coherence Tomography (SD-OCT) images for evaluation of putative
              retinal gliosis surface area and macular retinal nerve fiber
              layer (mRNFL) thickness. For all participants, correlations
              (adjusted for age and correlation between eyes) were assessed
              between retinal and blood-based biomarkers. A subgroup cohort of
              57 eyes from 32 participants with recent A$\beta$ positron
              emission tomography (PET) results, comprising 18 preclinical
              patients (A$\beta$ PET + ve, 32 eyes) and 14 controls (A$\beta$
              PET -ve, 25 eyes) with a mean age of 69 vs. 66, p = 0.06, was
              included for the assessment of a multimodal model to distinguish
              between the two groups. For this subgroup cohort, receiver
              operating characteristic (ROC) analysis was performed to compare
              the multimodal model of retinal and plasma biomarkers vs. each
              biomarker alone to distinguish between the two groups. RESULTS:
              Significant correlation was found between putative retinal
              gliosis and p-tau217 in the univariate mixed model ($\beta$ =
              0.48, p = 0.007) but not for the other plasma biomarkers (p >
              0.05). This positive correlation was also retained in the
              multivariate mixed model ($\beta$ = 0.43, p = 0.022). The
              multimodal ROC model based on retinal (gliosis area, inner
              inferior RNFL thickness, inner superior RNFL thickness, and inner
              nasal RNFL thickness) and plasma biomarkers (p-tau217 and
              A$\beta$42/A$\beta$40 ratio) had an excellent AUC of 0.97 (95\%
              CI = 0.93-1.01; p < 0.001) compared to unimodal models of retinal
              and plasma biomarkers. CONCLUSIONS: Our analyses show the
              potential of integrating retinal and blood-based biomarkers for
              improved detection and screening of preclinical AD.",
  journal  = "Alzheimers. Res. Ther.",
  volume   =  17,
  number   =  1,
  pages    = "19",
  month    =  jan,
  year     =  2025,
  keywords = "Early detection; Plasma biomarkers; Preclinical Alzheimer's
              disease; Putative retinal gliosis; Spectral domain optical
              coherence tomography",
  language = "en"
}

@ARTICLE{Byun2021-ra,
  title     = "Association of retinal changes with Alzheimer disease
               neuroimaging biomarkers in cognitively normal individuals",
  author    = "Byun, Min Soo and Park, Sung Wook and Lee, Jun Ho and Yi, Dahyun
               and Jeon, So Yeon and Choi, Hyo Jung and Joung, Haejung and
               Ghim, Un Hyung and Park, Un Chul and Kim, Yu Kyeong and Shin,
               Seong A and Yu, Hyeong Gon and Lee, Dong Young and {KBASE
               Research Group}",
  abstract  = "IMPORTANCE: Retinal biomarkers reflecting in vivo brain
               Alzheimer disease (AD) pathologic abnormalities could be a
               useful tool for screening cognitively normal (CN) individuals at
               the preclinical stage of AD. OBJECTIVES: To investigate the
               association of both functional and structural alterations of the
               retina with in vivo AD pathologic abnormalities in CN older
               adults and model a screening tool for detection of preclinical
               AD. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional
               study included a total of 49 CN individuals, and all assessment
               was done at the Seoul National University Hospital, Seoul, South
               Korea. All participants underwent complete ophthalmic
               examination, including swept-source optical coherence tomography
               (SS-OCT) and multifocal electroretinogram as well as
               amyloid-$\beta$ (A$\beta$) positron emission tomography and
               magnetic resonance imaging. Data were collected from January 1,
               2016, through October 31, 2017, and analyzed from February 1,
               2018, through June 30, 2020. MAIN OUTCOMES AND MEASURES: For
               structural parameters of the retina, the thickness of the macula
               and layer-specific thicknesses, including peripapillary retinal
               nerve fiber layer and ganglion cell-inner plexiform layer
               measured by SS-OCT, were used for analysis. For functional
               parameters of the retina, implicit time and amplitude of rings 1
               to 6 measured by multifocal electroretinogram were used.
               RESULTS: Of the 49 participants, 25 were women (51.0\%); mean
               (SD) age was 70.6 (9.4) years. Compared with 33 CN individuals
               without A$\beta$ deposition (A$\beta$-CN), the 16 participants
               with A$\beta$ (A$\beta$+CN) showed reduced inner nasal macular
               thickness (mean [SD], 308.9 [18.4] vs 286.1 [22.5] $\mu$m; P =
               .007) and retinal nerve fiber layer thickness, particularly in
               the inferior quadrant (133.8 [17.9] vs 103.8 [43.5] $\mu$m; P =
               .003). In addition, the A$\beta$+CN group showed prolonged
               implicit time compared with the A$\beta$-CN group, particularly
               in ring 5 (41.3 [4.0] vs 38.2 [1.3] milliseconds; P = .002).
               AD-related neurodegeneration was correlated with the thickness
               of the ganglion cell-inner plexiform layer only (r = 0.41, P =
               .005). The model to differentiate the A$\beta$+CN vs A$\beta$-CN
               groups derived from the results showed 90\% accuracy.
               CONCLUSIONS AND RELEVANCE: The findings of this study showing
               both functional as well as structural changes of retina measured
               by multifocal electroretinogram and SS-OCT in preclinical AD
               suggest the potential use of retinal biomarkers as a tool for
               early detection of in vivo AD pathologic abnormalities in CN
               older adults.",
  journal   = "JAMA Ophthalmol.",
  publisher = "American Medical Association (AMA)",
  volume    =  139,
  number    =  5,
  pages     = "548--556",
  month     =  may,
  year      =  2021,
  language  = "en"
}

@ARTICLE{Snyder2016-ot,
  title     = "Nonvascular retinal imaging markers of preclinical Alzheimer's
               disease",
  author    = "Snyder, Peter J and Johnson, Lenworth N and Lim, Yen Ying and
               Santos, Cl{\'a}udia Y and Alber, Jessica and Maruff, Paul and
               Fern{\'a}ndez, Brian",
  abstract  = "INTRODUCTION: In patients with Alzheimer's disease (AD) and mild
               cognitive impairment, structural changes in the retina (i.e.,
               reduced thicknesses of the ganglion cell and retinal nerve fiber
               layers and inclusion bodies that appear to contain beta-amyloid
               protein [Ab]) have been previously reported. We sought to
               explore whether anatomic retinal changes are detectable in the
               preclinical stage of AD. METHODS: A cross-sectional study (as
               part of an ongoing longitudinal cohort study) involving 63
               cognitively normal adults, all of whom have a parent with AD and
               subjective memory complaints. We compared neocortical amyloid
               aggregation (florbetapir PET imaging) to retinal spectral domain
               optical coherence tomography (SD-OCT) markers of possible
               disease burden. Retinal biomarkers, including the number and
               surface area of retinal inclusion bodies and the thickness of
               retinal neuronal layers, were compared across groups with high
               vs. low neocortical beta-amyloid load. RESULTS: The surface area
               of inclusion bodies increased as a function of cortical amyloid
               burden. Additionally, there was a trend toward a selective
               volume increase in the inner plexiform layer (IPL; a layer rich
               in cholinergic activity) of the retina in A$\beta$+ relative to
               A$\beta$- participants, and IPL volume was correlated with the
               surface area of retinal inclusion bodies. DISCUSSION: These
               initial results suggest that retinal imaging may be a potential
               cost-effective and noninvasive technique that can be used to
               identify those at-risk for AD. Layer-specific changes in the IPL
               and their association with surface area of inclusion bodies are
               discussed as a possible reflection of early inflammatory
               processes associated with cholinergic disruption and concurrent
               Ab accumulation in the neocortex.",
  journal   = "Alzheimers Dement. (Amst.)",
  publisher = "Wiley",
  volume    =  4,
  number    =  1,
  pages     = "169--178",
  month     =  oct,
  year      =  2016,
  keywords  = "Acetylcholine; Alzheimer's disease; Biomarkers; Cholinergic
               hypothesis; Inner plexiform layer; OCT; Optical Coherence
               Tomography; Preclinical AD; Retina",
  copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  language  = "en"
}

@inproceedings{eslami_pubmedclip_2023,
	address = {Dubrovnik, Croatia},
	title = {{PubMedCLIP}: {How} {Much} {Does} {CLIP} {Benefit} {Visual} {Question} {Answering} in the {Medical} {Domain}?},
	shorttitle = {{PubMedCLIP}},
	url = {https://aclanthology.org/2023.findings-eacl.88/},
	doi = {10.18653/v1/2023.findings-eacl.88},
	abstract = {Contrastive Language–Image Pre-training (CLIP) has shown remarkable success in learning with cross-modal supervision from extensive amounts of image–text pairs collected online. Thus far, the effectiveness of CLIP has been investigated primarily in general-domain multimodal problems. In this work, we evaluate the effectiveness of CLIP for the task of Medical Visual Question Answering (MedVQA). We present PubMedCLIP, a fine-tuned version of CLIP for the medical domain based on PubMed articles. Our experiments conducted on two MedVQA benchmark datasets illustrate that PubMedCLIP achieves superior results improving the overall accuracy up to 3\% in comparison to the state-of-the-art Model-Agnostic Meta-Learning (MAML) networks pre-trained only on visual data. The PubMedCLIP model with different back-ends, the source code for pre-training them and reproducing our MedVQA pipeline is publicly available at https://github.com/sarahESL/PubMedCLIP.},
	urldate = {2025-11-28},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {EACL} 2023},
	publisher = {Association for Computational Linguistics},
	author = {Eslami, Sedigheh and Meinel, Christoph and de Melo, Gerard},
	editor = {Vlachos, Andreas and Augenstein, Isabelle},
	month = may,
	year = {2023},
	pages = {1181--1193},
	file = {Full Text PDF:files/1684/Eslami 등 - 2023 - PubMedCLIP How Much Does CLIP Benefit Visual Question Answering in the Medical Domain.pdf:application/pdf},
}

@misc{wang2022medclipcontrastivelearningunpaired,
      title={MedCLIP: Contrastive Learning from Unpaired Medical Images and Text}, 
      author={Zifeng Wang and Zhenbang Wu and Dinesh Agarwal and Jimeng Sun},
      year={2022},
      eprint={2210.10163},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2210.10163}, 
}

@ARTICLE{Armstrong2021-cc,
  title     = "Retinal imaging findings in carriers with {PSEN1-associated}
               early-onset familial Alzheimer disease before onset of cognitive
               symptoms",
  author    = "Armstrong, Grayson W and Kim, Leo A and Vingopoulos, Filippos and others",
  abstract  = "Importance: Individuals with autosomal dominant mutations for
               Alzheimer disease are valuable in determining biomarkers present
               prior to the onset of cognitive decline, improving the ability
               to diagnose Alzheimer disease as early as possible. Optical
               coherence tomography (OCT) has surfaced as a potential
               noninvasive technique capable of analyzing central nervous
               system tissues for biomarkers of Alzheimer disease. Objective:
               To evaluate whether OCT can detect early retinal alterations in
               carriers of the presenilin 1 (PSEN1 [OMIM 104311]) E280A
               mutation who are cognitively unimpaired. Design, Setting, and
               Participants: A cross-sectional imaging study conducted from
               July 13, 2015, to September 16, 2020, included 10 carriers of
               the PSEN1 E280A mutation who were cognitively unimpaired and 10
               healthy noncarrier family members, all leveraged from a
               homogenous Colombian kindred. Statistical analysis was conducted
               from September 9, 2017, to September 16, 2020. Main Outcomes and
               Measures: Mixed-effects multiple linear regression was performed
               to compare the thickness values of the whole retina and
               individual retinal layers on OCT scans between mutation carriers
               and noncarriers. Simple linear-effects and mixed-effects
               multiple linear regression models were used to assess whether
               age was an effect modifier for PSEN1 mutation of amyloid $\beta$
               levels and retinal thickness, respectively. Fundus photographs
               were used to compare the number of arterial and venous branch
               points, arterial and venous tortuosity, and fractal dimension.
               Results: This study included 10 carriers of the PSEN1 E280A
               mutation who were cognitively unimpaired (7 women [70\%]; mean
               [SD] age, 36.3 [8.1] years) and 10 healthy noncarrier family
               members (7 women [70\%]; mean [SD] age, 36.4 [8.2] years).
               Compared with noncarrier controls, PSEN1 mutation carriers who
               were cognitively unimpaired had a generalized decrease in
               thickness of the whole retina as well as individual layers
               detected on OCT scans, with the inner nuclear layer (outer
               superior quadrant, $\beta$ = -3.06; P = .007; outer inferior
               quadrant, $\beta$ = -2.60; P = .02), outer plexiform layer
               (outer superior quadrant, $\beta$ = -3.44; P = .03), and outer
               nuclear layer (central quadrant, $\beta$ = -8.61; P = .03; inner
               nasal quadrant, $\beta$ = -8.39; P = .04; inner temporal
               quadrant, $\beta$ = -9.39; P = .02) showing the greatest amount
               of statistically significant thinning. Age was a significant
               effect modifier for the association between PSEN1 mutation and
               amyloid $\beta$ levels in cortical regions ($\beta$ = 0.03; P =
               .001) but not for the association between PSEN1 mutation and
               retinal thickness. No statistical difference was detected in any
               of the vascular parameters studied. Conclusions and Relevance:
               These findings suggest that OCT can detect functional and
               morphologic changes in the retina of carriers of familial
               Alzheimer disease who are cognitively unimpaired several years
               before clinical onset, suggesting that OCT findings and retinal
               vascular parameters may be biomarkers prior to the onset of
               cognitive decline.",
  journal   = "JAMA Ophthalmol.",
  publisher = "American Medical Association (AMA)",
  volume    =  139,
  number    =  1,
  pages     = "49--56",
  month     =  jan,
  year      =  2021,
  language  = "en"
}

@ARTICLE{Ong2014-xh,
  title     = "Retinal vascular fractals and cognitive impairment",
  author    = "Ong, Yi-Ting and Hilal, Saima and Cheung, Carol Yim-Lui and others",
  abstract  = "BACKGROUND: Retinal microvascular network changes have been
               found in patients with age-related brain diseases such as stroke
               and dementia including Alzheimer's disease. We examine whether
               retinal microvascular network changes are also present in
               preclinical stages of dementia. METHODS: This is a
               cross-sectional study of 300 Chinese participants (age: $\geq$60
               years) from the ongoing Epidemiology of Dementia in Singapore
               study who underwent detailed clinical examinations including
               retinal photography, brain imaging and neuropsychological
               testing. Retinal vascular parameters were assessed from optic
               disc-centered photographs using a semiautomated program. A
               comprehensive neuropsychological battery was administered, and
               cognitive function was summarized as composite and
               domain-specific Z-scores. Cognitive impairment no dementia
               (CIND) and dementia were diagnosed according to standard
               diagnostic criteria. RESULTS: Among 268 eligible nondemented
               participants, 78 subjects were categorized as CIND-mild and 69
               as CIND-moderate. In multivariable adjusted models, reduced
               retinal arteriolar and venular fractal dimensions were
               associated with an increased risk of CIND-mild and
               CIND-moderate. Reduced fractal dimensions were associated with
               poorer cognitive performance globally and in the specific
               domains of verbal memory, visuoconstruction and visuomotor
               speed. CONCLUSION: A sparser retinal microvascular network,
               represented by reduced arteriolar and venular fractal
               dimensions, was associated with cognitive impairment, suggesting
               that early microvascular damage may be present in preclinical
               stages of dementia.",
  journal   = "Dement. Geriatr. Cogn. Dis. Extra",
  publisher = "S. Karger AG",
  volume    =  4,
  number    =  2,
  pages     = "305--313",
  month     =  may,
  year      =  2014,
  keywords  = "Cognition; Cognitive impairment; Fractal dimension;
               Microvasculature; Retina",
  copyright = "https://creativecommons.org/licenses/by-nc/3.0/",
  language  = "en"
}

@ARTICLE{Valenti2011-dq,
  title     = "Alzheimer's disease and glaucoma: imaging the biomarkers of
               neurodegenerative disease",
  author    = "Valenti, Denise A",
  abstract  = "Imaging through the visual system in Alzheimer's disease, with
               the technology currently in widespread use for the diagnosis and
               management of eye disease such as glaucoma and macular
               degeneration, is proving to be promising. In vivo cross-section
               imaging during an annual comprehensive eye exam has been
               available for a decade for glaucoma and macular degeneration,
               and this same imaging, using Optical Coherence Tomography, has
               been demonstrated to show deficits specific to AD and mild
               cognitive impairment. These deficits are in the form of nerve
               fiber layer tissue drop out in the retina and optic nerve. The
               retrograde loss of nerve fiber layer tissue in the retina and
               optic nerve may be an early biomarker of AD, and these deficits
               in the nerve fiber layer of the retina and optic nerve may be
               the earliest sign of AD, even prior to damage to the hippocampal
               region that impacts memory.",
  journal   = "Int. J. Alzheimers. Dis.",
  publisher = "Hindawi Limited",
  volume    =  2010,
  pages     = "793931",
  month     =  jan,
  year      =  2011,
  language  = "en"
}

@ARTICLE{Sharafi2019-ko,
  title     = "Vascular retinal biomarkers improves the detection of the likely
               cerebral amyloid status from hyperspectral retinal images",
  author    = "Sharafi, Sayed Mehran and Sylvestre, Jean-Philippe and
               Chevrefils, Claudia and Soucy, Jean-Paul and Beaulieu, Sylvain
               and Pascoal, Tharick A and Arbour, Jean Daniel and Rh{\'e}aume,
               Marc-Andr{\'e} and Robillard, Alain and Chayer, C{\'e}line and
               Rosa-Neto, Pedro and Mathotaarachchi, Sulantha S and Nasreddine,
               Ziad S and Gauthier, Serge and Lesage, Fr{\'e}d{\'e}ric",
  abstract  = "INTRODUCTION: This study investigates the relationship between
               retinal image features and $\beta$-amyloid (A$\beta$) burden in
               the brain with the aim of developing a noninvasive method to
               predict the deposition of A$\beta$ in the brain of patients with
               Alzheimer's disease. METHODS: Retinal images from 20 cognitively
               impaired and 26 cognitively unimpaired cases were acquired (3
               images per subject) using a hyperspectral retinal camera. The
               cerebral amyloid status was determined from binary reads by a
               panel of 3 expert raters on 18F-florbetaben positron-emission
               tomography (PET) studies. Image features from the hyperspectral
               retinal images were calculated, including vessels tortuosity and
               diameter and spatial-spectral texture measures in different
               retinal anatomical regions. RESULTS: Retinal venules of
               amyloid-positive subjects (A$\beta$+) showed a higher mean
               tortuosity compared with the amyloid-negative (A$\beta$-)
               subjects. Arteriolar diameter of A$\beta$+ subjects was found to
               be higher than the A$\beta$- subjects in a zone adjacent to the
               optical nerve head. Furthermore, a significant difference
               between texture measures built over retinal arterioles and their
               adjacent regions were observed in A$\beta$+ subjects when
               compared with the A$\beta$-. A classifier was trained to
               automatically discriminate subjects combining the extracted
               features. The classifier could discern A$\beta$+ subjects from
               A$\beta$- subjects with an accuracy of 85\%. DISCUSSION:
               Significant differences in texture measures were observed in the
               spectral range 450 to 550 nm which is known as the spectral
               region known to be affected by scattering from amyloid
               aggregates in the retina. This study suggests that the inclusion
               of metrics related to the retinal vasculature and tissue-related
               textures extracted from vessels and surrounding regions could
               improve the discrimination performance of the cerebral amyloid
               status.",
  journal   = "Alzheimers Dement. (N. Y.)",
  publisher = "Wiley",
  volume    =  5,
  number    =  1,
  pages     = "610--617",
  month     =  oct,
  year      =  2019,
  keywords  = "Alzheimer; Beta amyloid; Image processing; Machine learning;
               Multispectral fundus imaging; Retina",
  copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  language  = "en"
}

@ARTICLE{Frost2013-el,
  title     = "Retinal vascular biomarkers for early detection and monitoring
               of Alzheimer's disease",
  author    = "Frost, S and Kanagasingam, Y and Sohrabi, H and Vignarajan, J
               and Bourgeat, P and Salvado, O and Villemagne, V and Rowe, C C
               and Macaulay, S Lance and Szoeke, C and Ellis, K A and Ames, D
               and Masters, C L and Rainey-Smith, S and Martins, R N and {AIBL
               Research Group}",
  abstract  = "The earliest detectable change in Alzheimer's disease (AD) is
               the buildup of amyloid plaque in the brain. Early detection of
               AD, prior to irreversible neurological damage, is important for
               the efficacy of current interventions as well as for the
               development of new treatments. Although PiB-PET imaging and CSF
               amyloid are the gold standards for early AD diagnosis, there are
               practical limitations for population screening. AD-related
               pathology occurs primarily in the brain, but some of the
               hallmarks of the disease have also been shown to occur in other
               tissues, including the retina, which is more accessible for
               imaging. Retinal vascular changes and degeneration have
               previously been reported in AD using optical coherence
               tomography and laser Doppler techniques. This report presents
               results from analysis of retinal photographs from AD and healthy
               control participants from the Australian Imaging, Biomarkers and
               Lifestyle (AIBL) Flagship Study of Ageing. This is the first
               study to investigate retinal blood vessel changes with respect
               to amyloid plaque burden in the brain. We demonstrate
               relationships between retinal vascular parameters, neocortical
               brain amyloid plaque burden and AD. A number of RVPs were found
               to be different in AD. Two of these RVPs, venular branching
               asymmetry factor and arteriolar length-to-diameter ratio, were
               also higher in healthy individuals with high plaque burden (P =
               0.01 and P = 0.02 respectively, after false discovery rate
               adjustment). Retinal photographic analysis shows potential as an
               adjunct for early detection of AD or monitoring of
               AD-progression or response to treatments.",
  journal   = "Transl. Psychiatry",
  publisher = "Springer Science and Business Media LLC",
  volume    =  3,
  number    =  2,
  pages     = "e233",
  month     =  feb,
  year      =  2013,
  language  = "en"
}
