Cocca S, Pontillo G, Grande G, Conigliaro R. Artificial intelligence in detection of small bowel lesions and their bleeding risk: A new step forward. World J Gastroenterol 2024; 30(18): 2482-2484 [PMID: 38764765 DOI: 10.3748/wjg.v30.i18.2482]
Corresponding Author of This Article
Silvia Cocca, MD, PhD, Doctor, Gastroenterology and Endoscopy Unit, Azienda Ospedaliero Universitaria Policlinico di Modena, Via Pietro Giardini 1355, Modena 41121, Italy. silvia.cocca@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. May 14, 2024; 30(18): 2482-2484 Published online May 14, 2024. doi: 10.3748/wjg.v30.i18.2482
Artificial intelligence in detection of small bowel lesions and their bleeding risk: A new step forward
Silvia Cocca, Giuseppina Pontillo, Giuseppe Grande, Rita Conigliaro
Silvia Cocca, Rita Conigliaro, Gastroenterology and Endoscopy Unit, Azienda Ospedaliero Universitaria Policlinico di Modena, Modena 41121, Italy
Giuseppina Pontillo, Gastroenterology and Endoscopy Unit, Presidio Ospedaliero San Giuseppe Moscati (Aversa, CE) - ASL Caserta, Caserta 81100, Italy
Giuseppe Grande, Department of Gastroenterology and Digestive Endoscopy, Azienda Ospedaliero Universitaria di Modena, Modena 41121, Italy
Author contributions: Cocca S and Conigliaro R contributed to conceptualization; Cocca S, Pontillo G and Grande G contributed to writing – review and editing.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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/
Corresponding author: Silvia Cocca, MD, PhD, Doctor, Gastroenterology and Endoscopy Unit, Azienda Ospedaliero Universitaria Policlinico di Modena, Via Pietro Giardini 1355, Modena 41121, Italy. silvia.cocca@gmail.com
Received: February 3, 2024 Revised: March 9, 2024 Accepted: April 17, 2024 Published online: May 14, 2024 Processing time: 97 Days and 22.5 Hours
Abstract
The present letter to the editor is related to the study with the title “Automatic detection of small bowel (SB) lesions with different bleeding risk based on deep learning models”. Capsule endoscopy (CE) is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate. The development of artificial intelligence (AI) in CE could simplify physicians’ tasks. The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk, and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.
Core Tip: The development of artificial intelligence (AI) in capsule endoscopy could simplify physicians tasks by reducing the number of frames that need to be analyzed by the physician. The novel model proposed in the retrospective study by Zhang et al. could be able to identify various small bowel lesions and their bleeding risk. More prospective studies are needed to validate this promising AI-model.
