{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:48:16Z","timestamp":1791395296951,"version":"4.3.3"},"reference-count":28,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["Hepatology"],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Approach and Results<\/jats:title>\n                    <jats:p>Here, we describe a machine learning (ML)\u2010based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML\u2010based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver\u2010related clinical events. We developed a heterogeneity\u2010sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hep.31750","type":"journal-article","created":{"date-parts":[[2021,2,13]],"date-time":"2021-02-13T22:52:22Z","timestamp":1613256742000},"page":"133-147","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":186,"title":["A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH"],"prefix":"10.1097","volume":"74","author":[{"given":"Amaro","family":"Taylor\u2010Weiner","sequence":"first","affiliation":[{"name":"PathAI,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harsha","family":"Pokkalla","sequence":"additional","affiliation":[{"name":"PathAI,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Han","sequence":"additional","affiliation":[{"name":"Gilead Sciences, Inc.,Foster City,CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Jia","sequence":"additional","affiliation":[{"name":"Gilead Sciences, Inc.,Foster City,CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Huss","sequence":"additional","affiliation":[{"name":"Gilead Sciences, Inc.,Foster City,CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuhan","family":"Chung","sequence":"additional","affiliation":[{"name":"Gilead Sciences, Inc.,Foster City,CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hunter","family":"Elliott","sequence":"additional","affiliation":[{"name":"PathAI,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Glass","sequence":"additional","affiliation":[{"name":"PathAI,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kishalve","family":"Pethia","sequence":"additional","affiliation":[{"name":"PathAI,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oscar","family":"Carrasco\u2010Zevallos","sequence":"additional","affiliation":[{"name":"PathAI,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chinmay","family":"Shukla","sequence":"additional","affiliation":[{"name":"PathAI,Boston,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Urmila","family":"Khettry","sequence":"additional","affiliation":[{"name":"Lahey Hospital & Medical Center (Emeritus),Burlington,MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Najarian","sequence":"additional","affiliation":[{"name":"University Gastroenterology,Portsmouth,RI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ross","family":"Taliano","sequence":"additional","affiliation":[{"name":"Warren Alpert Medical School of Brown University,Providence,RI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G. 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