Publication Name	Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID	117988
Country	United States

Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Multimodal artificial intelligence in capsule endoscopy: Integrating video and sensor data for advanced gastrointestinal diagnostics
Manuscript Source	Invited Manuscript
All Author List	Rishi Chowdhary, Param Darpan Sheth, Insiya Mohammed Rampurawala, Chitresh Kapadia, Chirag Vohra, Rahul Chowdhary, Kirti Arora, Varna Taranikanti, Ashita Rukmini Vuthaluru, Omesh Goyal and Manjeet Kumar Goyal
Funding Agency and Grant Number
Corresponding Author	Manjeet Kumar Goyal, DM, Doctorate Student, MD, Department of Internal Medicine, Cleveland Clinic Akron General Hospital, 1, Akron General Avenue, AKron, Akron, OH 44308, United States. manjeetgoyal@gmail.com
Key Words	Capsule endoscopy; Artificial intelligence; Multimodal artificial intelligence; Deep learning; Convolutional neural networks; Gastrointestinal diagnostics; Data fusion; Lesion detection
Core Tip	Capsule endoscopy (CE) generates thousands of images per study, creating diagnostic and workflow challenges due to manual interpretation and localization errors. The integration of multimodal artificial intelligence combining visual data with sensor inputs such as inertial measurement units, magnetic trackers, and physiological monitors has significantly improved lesion detection, localization, and reading efficiency. Advanced architectures achieve sub-millimeter localization accuracy and > 95% diagnostic precision. These developments represent a paradigm shift in CE, transforming it from a passive imaging tool into an intelligent, context-aware diagnostic platform with the potential to enhance accuracy, reduce reading time, and standardize interpretation across clinicians.
Citation	Chowdhary Ri, Sheth PD, Rampurawala IM, Kapadia C, Vohra C, Chowdhary Ra, Arora K, Taranikanti V, Vuthaluru AR, Goyal O, Goyal MK. Multimodal artificial intelligence in capsule endoscopy: Integrating video and sensor data for advanced gastrointestinal diagnostics. Artif Intell Gastrointest Endosc 2026; In press

Received	2025-12-22 08:06
Peer-Review Started	2025-12-22 08:06
First Decision by Editorial Office Director	2026-01-07 08:43
Return for Revision	2026-01-07 08:43
Revised	2026-01-08 19:37
Publication Fee Transferred
Second Decision by Editor	2026-01-22 02:33
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-22 06:08
Articles in Press	2026-01-22 06:08
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2689-7164 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
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