{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T21:35:54Z","timestamp":1789421754544,"version":"build-2803163510"},"reference-count":42,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T00:00:00Z","timestamp":1623801600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Liver International"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aims<\/jats:title><jats:p>While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Embase, MEDLINE, Web of Science, and IEEE Xplore databases were used to identify studies that reported on the accuracy of DL for classification of liver fibrosis on noninvasive imaging. The search keywords were \u201cliver or hepatic,\u201d \u201cfibrosis or cirrhosis,\u201d and \u201cneural or DL networks.\u201d Risk of bias and applicability were evaluated using the QUADAS\u20102 tool.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Sixteen studies were retrieved. Imaging modalities included ultrasound (n\u00a0=\u00a010), computed tomography (n\u00a0=\u00a03), and magnetic resonance imaging (n\u00a0=\u00a03). The studies analyzed a total of 40\u00a0405 radiological images from 15\u00a0853 patients. All but two of the studies were retrospective. In most studies the \u201cground truth\u201d reference was the METAVIR score for pathological staging (n\u00a0=\u00a09.56%). The majority of the studies reported an accuracy &gt;85% when compared to histopathology. Fourteen studies (87.5%) had a high risk of bias and concerns regarding applicability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Deep learning has the potential to play an emerging role in liver fibrosis classification. Yet, it is still limited by a relatively small number of retrospective studies. Clinicians should facilitate the use of this technology by sharing databases and standardized reports. This may optimize the noninvasive evaluation of liver fibrosis on a large scale.<\/jats:p><\/jats:sec>","DOI":"10.1111\/liv.14966","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T04:01:36Z","timestamp":1621396896000},"page":"2269-2278","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Deep learning for noninvasive liver fibrosis classification: A systematic review"],"prefix":"10.1111","volume":"41","author":[{"given":"Roi","family":"Anteby","sequence":"first","affiliation":[{"name":"School of Public Health Harvard University Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eyal","family":"Klang","sequence":"additional","affiliation":[{"name":"Department of Population Health Science and Policy Institute for Healthcare Delivery Science New York NY USA"},{"name":"Sackler Medical School Tel Aviv University Tel Aviv Israel"},{"name":"Deep Vision Lab The 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