{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T05:38:13Z","timestamp":1791005893843,"version":"4.1.0"},"reference-count":42,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE0113700"],"award-info":[{"award-number":["2019YFE0113700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82070588"],"award-info":[{"award-number":["82070588"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Aliment Pharmacol Ther"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Liver fibrosis is the strongest histological risk factor for liver\u2010related complications and mortality in metabolic dysfunction\u2010associated fatty liver disease (MAFLD). Second harmonic generation\/two\u2010photon excitation fluorescence (SHG\/TPEF) is a powerful tool for label\u2010free two\u2010dimensional and three\u2010dimensional tissue visualisation that shows promise in liver fibrosis assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To investigate combining multi\u2010photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy\u2010confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre\u2010processed images and test data sets. Multi\u2010layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3\u20104 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3\u20104 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/apt.17635","type":"journal-article","created":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T02:46:59Z","timestamp":1688525219000},"page":"573-584","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["<scp>AutoFibroNet<\/scp>\n                    : A deep learning and multi\u2010photon microscopy\u2010derived automated network for liver fibrosis quantification in\n                    <scp>MAFLD<\/scp>"],"prefix":"10.1111","volume":"58","author":[{"given":"Huiling","family":"Zhan","sequence":"first","affiliation":[{"name":"School of Science Jimei University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1432-4909","authenticated-orcid":false,"given":"Siyu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Engineering, Jimei University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology The First Affiliated Hospital of Wenzhou Medical University  Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangxing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science Jimei University  Xiamen 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Nanjing University, Medical School  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xitai","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Metabolic and Bariatric Surgery The Affiliated Drum Tower Hospital of Nanjing University, Medical School  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guorong","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Computer Engineering, Jimei University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4984-2631","authenticated-orcid":false,"given":"Ming\u2010Hua","family":"Zheng","sequence":"additional","affiliation":[{"name":"MAFLD Research Center, Department of Hepatology The First Affiliated Hospital of Wenzhou Medical University  Wenzhou China"},{"name":"Institute of Hepatology Wenzhou Medical University  Wenzhou China"},{"name":"Key Laboratory of Diagnosis and Treatment for 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