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

Table 1 Demographic characteristics of patients, n (%)/mean ± SD

Hospital group	Diagnostic group	Male	Age (years)
Zhongshan cohort	PA-HSOS (n = 39)	27 (69.23)	62.62 ± 12.33
	Budd-Chiari syndrome (n = 51)	29 (56.86)	44.98 ± 11.84
	Hepatitis B cirrhosis (n = 97)	73 (75.26)	53.29 ± 11.12
	Total (n = 187)	129 (68.98)	52.97 ± 13.02
Zhengzhou cohort	PA-HSOS (n = 28)	19 (67.86)	66.07 ± 9.25
	Budd-Chiari syndrome (n = 10)	6 (60.00)	55.9 ± 11.06
	Hepatitis B cirrhosis (n = 10)	8 (80)	49.60 ± 13.28
	Total (n = 48)	33 (68.75)	60.52 ± 12.43
Total	(n = 235)	162 (68.94)	54.37 ± 13.23

PA-HSOS: Pyrrolizidine-alkaloid-induced hepatic sinusoidal obstruction syndrome.

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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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