Deep learning for rare disease: A scoping review

Junghwan Lee

Abstract:

Although individually rare, collectively more than 7,000 rare diseases affect about 10% of patients.
Each of the rare diseases impacts the quality of life for patients and their families, and incurs
significant societal costs. The low prevalence of each rare disease causes formidable challenges in
accurately diagnosing and caring for these patients and engaging participants in research to
advance treatments. Deep learning has advanced many scientific fields and has been applied to
many healthcare tasks. This study reviewed the current uses of deep learning to advance rare
disease research. Among the 332 reviewed articles, we found that deep learning has been actively
used for rare neoplastic diseases (250/332), followed by rare genetic diseases (170/332) and rare
neurological diseases (127/332). Convolutional neural networks (307/332) were the most
frequently used deep learning architecture, presumably because image data were the most
commonly available data type in rare disease research. Diagnosis is the main focus of rare disease
research using deep learning (263/332). We summarized the challenges and future research
directions for leveraging deep learning to advance rare disease research.