Publication Name	World Journal of Gastroenterology
Manuscript ID	116105
Country	Taiwan

Category	Multidisciplinary Sciences
Manuscript Type	Observational Study
Article Title	Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks
Manuscript Source	Unsolicited Manuscript
All Author List	Hsin-Yu Kuo, Kun-Hua Lee, Chu-Kuang Chou, Arvind Mukundan, Riya Karmakar, Tsung-Hsien Chen, Thong-Lin Wang, Ping-Hung Liu and Hsiang-Chen Wang
Funding Agency and Grant Number	Funding Agency Grant Number National Science and Technology Council of the Republic of China NSTC 113-2221-E-194-011-MY3 National Science and Technology Council of the Republic of China NSTC 114-2314-B-006-095-MY3 National Cheng Kung University Hospital NCKUH-11404023 Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation-National Chung Cheng University Joint Research Program and Kaohsiung Armed Forces General Hospital Research Program, Research Center on Artificial Intelligence and Sustainability, National Chung Cheng University, Taiwan under the “Generative Digital Twin System Design for Sustainable Smart City Development in Taiwan” KAFGH_D_115045
Corresponding Author	Hsiang-Chen Wang, Professor, Department of Mechanical Engineering, National Chung Cheng University, No. 168 University Road, Chiayi 62102, Taiwan. hcwang@ccu.edu.tw
Key Words	Capsule endoscopy; Small intestinal bleeding; Convolutional neural networks; Long short term memory networks; Temporal modeling
Core Tip	This study proposes a practical deep learning pipeline that combines convolutional neural networks for spatial feature extraction with long short-term memory networks for temporal modeling to detect small intestinal bleeding in capsule endoscopy videos. Using datasets from a clinical cohort and the Kvasir-Capsule collection, we show that models trained with richer multiclass labels yield more informative features for sequential prediction, enabling higher long short-term memory accuracy and lower temporal error than binary setups. The approach delivers consistent frame-by-frame detection performance with clinically feasible processing speed, highlights the value of leveraging temporal dependencies beyond single-frame analysis, and outlines a path toward interpretable, efficient triage in capsule endoscopy workflows.
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; In press

Received	2025-11-03 07:05
Peer-Review Started	2025-11-03 07:05
First Decision by Editorial Office Director	2025-12-03 10:28
Return for Revision	2025-12-03 10:28
Revised	2025-12-14 02:53
Publication Fee Transferred	2025-12-20 12:59
Second Decision by Editor	2026-01-27 02:37
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-27 08:33
Articles in Press	2026-01-27 08:33
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Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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