BiliQML: A supervised machine-learning model to quantify biliary forms from digitized whole-slide liver histopathological images.

The progress of research focused on cholangiocytes and the biliary tree during development and following injury is hindered by limited available quantitative methodologies. Current techniques include two-dimensional standard histological cell-counting approaches, which are rapidly performed error-prone and lack architectural context; or three-dimensional analysis of the biliary tree in opacified livers, which introduce technical issues along with minimal quantitation. The present study aims to fill these quantitative gaps with a supervised machine learning model (BiliQML) able to quantify biliary forms in the liver of anti-Keratin 19 antibody-stained whole slide images. Training utilized 5,019 researcher-labeled biliary forms, which following feature selection, and algorithm optimization, generated an F-score of 0.87. Application of BiliQML on seven separate cholangiopathy models; genetic (Afp-CRE;Pkd1l1null/Fl, Alb-CRE;Rbp-jkfl/fl, Albumin-CRE; ROSANICD), surgical (bile duct ligation), toxicological (3,5-diethoxycarbonyl-1,4-dihydrocollidine), and therapeutic (Cyp2c70-/- with ileal bile acid transporter inhibition), allowed for a means to validate the capabilities, and utility of this platform. The results from BiliQML quantification revealed biological and pathological differences across these seven diverse models indicate a highly sensitive, robust, and scalable methodology for the quantification of distinct biliary forms. BiliQML is the first comprehensive machine-learning platform for biliary form analysis, adding much needed morphologic context to standard immunofluorescence - based histology, and provides clinical and basic-science researchers a novel tool for the characterization of cholangiopathies.