{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:59:57Z","timestamp":1790315997593,"version":"4.1.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>\n                    Automated analysis of whole-slide images may support objective quantification of selected lesions in experimental liver pathology. Using H&amp;amp;E-stained whole-slide images from rats exposed to thioacetamide (TAA), we developed a deep-learning framework for whole-slide segmentation and quantification of two prespecified targets: fibrosis and a single pooled class of pathologist-defined proliferative\/neoplastic regions. Benign proliferation, dysplasia\/preneoplasia, and malignant neoplasia were not annotated or trained as separate subclasses. The available project archive did not contain an auditable whole-slide- or animal-level training\/validation manifest; the available training code implements a patch-level random split. The framework generated fibrosis proportion, proliferative\/neoplastic-region proportion, and the count of spatially separated positive regions for each whole-slide image. These model-derived measurements were used to describe changes across experimental time points and to explore differences between the TAA and TAA\u202f+\u202fH\n                    <jats:sub>2<\/jats:sub>\n                    groups. The TAA\u202f+\u202fH\n                    <jats:sub>2<\/jats:sub>\n                    group showed a lower model-derived burden of the two selected lesion categories. The framework does not assess the full spectrum of TAA-associated liver pathology and should not be interpreted as an independently validated diagnostic classifier. Independent animal-level validation is required before diagnostic or therapeutic claims can be made.\n                  <\/jats:p>","DOI":"10.3389\/frai.2026.1884918","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:33:00Z","timestamp":1790314380000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Whole-slide deep-learning quantification of fibrosis and pathologist-defined proliferative\/neoplastic regions in a thioacetamide-induced rat liver model"],"prefix":"10.3389","volume":"9","author":[{"given":"Feng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology","place":["Xuzhou, China"]},{"name":"School of Information Engineering, Yulin University","place":["Yulin, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yating","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yating","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deqiang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology","place":["Xuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanzhen","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine","place":["Shanghai, China"]},{"name":"Allergy and Cancer Research Center, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Changhai Hospital, Naval Medical University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Cheng","sequence":"additional","affiliation":[{"name":"Allergy and Cancer Research Center, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e45594","DOI":"10.7759\/cureus.45594","article-title":"Artificial intelligence in healthcare: perception and reality","volume":"15","author":"Akinrinmade","year":"2023","journal-title":"Cureus"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3892\/br.2024.1889","article-title":"Artificial intelligence in healthcare (review)","volume":"22","author":"Alhejaily","year":"2025","journal-title":"Biomed. 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