A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.

Amaro Taylor-WeinerHarsha PokkallaLing HanCatherine JiaRyan HussChuhan ChungHunter ElliottBenjamin GlassKishalve PethiaOscar Carrasco-ZevallosChinmay ShuklaUrmila KhettryRobert NajarianRoss TalianoG Mani SubramanianRobert P MyersIlan WapinskiAditya KhoslaMurray ResnickMichael C MontaltoQuentin Mark Anstee Vincent Wai-Sun WongMichael TraunerEric J LawitzStephen A HarrisonTakeshi OkanoueManuel RomeroZachary GoodmanRohit LoombaAndrew H BeckZobair M Younossi

Published in: Hepatology (Baltimore, Md.) (2021)

Our ML method has shown reproducibility and sensitivity and was prognostic for disease progression, demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of therapies.

Keyphrases

machine learning
end stage renal disease
ejection fraction
chronic kidney disease
prognostic factors
high resolution
single cell
artificial intelligence
patient reported outcomes