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

ABSTRACT:

Motivation

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).

Results

We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled

SUBMITTER: Feng C 

PROVIDER: S-EPMC10796177 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature