Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis

Chunyue Feng, Kok Haur Ong, David M. Young, Bingxian Chen, Longjie Li, Xinmi Huo, Haoda Lu, Weizhong Gu, Fei Liu, Hongfeng Tang, Manli Zhao, Min Yang, Kun Zhu, Limin Huang, Qiang Wang, Gabriel Pik Liang Marini, Kun Gui, Hao Han, Stephan J. Sanders, Lin Li et al. (2 additional authors not shown)

Abstract: Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD).