Development and validation of a deep-learning-based diagnostic model for drug-induced liver injury using computed tomography images

doi:10.3748/wjg.v32.i15.114778

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastroenterol. Apr 21, 2026; 32(15): 114778
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.114778

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Figure 1 Pipeline of deep-learning-based pyrrolizidine-alkaloid induced hepatic sinusoidal obstruction syndrome classification based on computed tomography images. CT: Computed tomography; DL: Deep learning; AUC: Area under the curve; ROC: Receiver operating characteristic.

Citation: Wang SY, Yin SQ, Yang JY, Ji MY, Zeng XQ, Rao SX, Lv MZ, Bao J, Wang MN, Gao H. Development and validation of a deep-learning-based diagnostic model for drug-induced liver injury using computed tomography images. World J Gastroenterol 2026; 32(15): 114778
URL: https://www.wjgnet.com/1007-9327/full/v32/i15/114778.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i15.114778

Wang SY, Yin SQ, Yang JY, Ji MY, Zeng XQ, Rao SX, Lv MZ, Bao J, Wang MN, Gao H. Development and validation of a deep-learning-based diagnostic model for drug-induced liver injury using computed tomography images. World J Gastroenterol 2026; 32(15): 114778 [DOI: 10.3748/wjg.v32.i15.114778]

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