End-to-end interstitial fibrosis assessment of kidney biopsies with a machine learning-based model

Liu, Zhi-YongZhi-YongLiuLin, Chi-HungChi-HungLinWang, Hsiang-ShengHsiang-ShengWangWen, Mei-ChinMei-ChinWenWEI-CHOU LINHuang, Shun-ChenShun-ChenHuangTu, Kun-HuaKun-HuaTuKuo, Chang-FuChang-FuKuoChen, Tai-DiTai-DiChen2023-01-172023-01-172022-10-190931-0509https://scholars.lib.ntu.edu.tw/handle/123456789/627338The extent of interstitial fibrosis in the kidney not only correlates with renal function at the time of biopsy but also predicts future renal outcome. However, its assessment by pathologists lacks good agreement. The aim of this study is to construct a machine learning-based model that enables automatic and reliable assessment of interstitial fibrosis in human kidney biopsies.eninterstitial fibrosis; machine learning; reliability; reproducibility; whole-slide imaging[SDGs]SDG3End-to-end interstitial fibrosis assessment of kidney biopsies with a machine learning-based modeljournal article10.1093/ndt/gfac143355126042-s2.0-85140415736WOS:000807932800001https://api.elsevier.com/content/abstract/scopus_id/85140415736