Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.

ABSTRACT: Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time-consuming tasks require a more accurate and reproducible quantitative analysis method. We established derivation and validation cohorts composed of 400 Chinese patients with immunoglobulin A nephropathy (IgAN) retrospectively. Deep convolutional neural networks and biomedical image processing algorithms were implemented to locate glomeruli, identify glomerular lesions (global and segmental glomerular sclerosis, crescent, and none of the above), identify and quantify different intrinsic glomerular cells, and assess a network-based mesangial hypercellularity score in periodic acid-Schiff (PAS)-stained slides. Our framew

SUBMITTER: Zeng C 

PROVIDER: S-EPMC7496925 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature