Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks

doi:10.3748/wjg.v32.i15.116105

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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): 116105
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116105

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Figure 4 Clinical workflow for diagnosis and treatment of unexplained gastrointestinal bleeding. The figure summarizes how capsule endoscopy is integrated into the diagnostic process, from initial patient evaluation to bleeding source localization and subsequent therapeutic intervention. a: Initial patient evaluation; b: Bidirectional endoscopy; c: Small bowel evaluation; d: Bleeding source localization; e: Therapeutic intervention. DAE: Device-assisted enteroscopy; SBCE: Small-bowel capsule endoscopy; CTE: Computed tomography enterography.

Citation: Kuo HY, Lee KH, Chou CK, Mukundan A, Karmakar R, Chen TH, Wang TL, Liu PH, Wang HC. Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks. World J Gastroenterol 2026; 32(15): 116105
URL: https://www.wjgnet.com/1007-9327/full/v32/i15/116105.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i15.116105

Kuo HY, Lee KH, Chou CK, Mukundan A, Karmakar R, Chen TH, Wang TL, Liu PH, Wang HC. Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks. World J Gastroenterol 2026; 32(15): 116105 [DOI: 10.3748/wjg.v32.i15.116105]

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