| 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/ |
| Copyright |
The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| 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 |
| ORCID |
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| Funding Agency and Grant Number |
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| 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. |
| Publish Date |
2026-03-03 02:36 |
| Citation |
Chowdhary R, Sheth PD, Rampurawala IM, Kapadia C, Vohra C, Chowdhary R, 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; 7(1): 117988 |
| URL |
https://www.wjgnet.com/2689-7164/full/v7/i1/117988.htm |
| DOI |
https://dx.doi.org/10.37126/aige.v7.i1.117988 |
