Letter to the Editor Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
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, 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
ORCID number: Silvia Cocca (0000-0002-0642-8054); Giuseppe Grande (0000-0003-3907-9740); Rita Conigliaro (0000-0002-2785-5952).
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


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.

Key Words: Capsule endoscopy, Small bowel, Artificial intelligence, Bleeding risk, Vascular lesions

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.


We were intrigued by the recently published paper by Zhang et al[1] evaluating the accuracy of a new classification and detection deep learning model to identify different types of small bowel (SB) lesions and their bleeding risk. The proposed model showed high diagnostic sensitivity and accuracy, and it resulted in a significant reduction of physician’s reading time.

Capsule endoscopy (CE) is the main tool to assess SB diseases[2] and, since its introduction, the capsule readers spend on average from 30 to 120 min to review and interpret full-length CE videos containing thousands of frames, thus leading to a gradual reduction of reader attention. The development of artificial intelligence (AI) and its application in SB diseases detection, represents a hot topic, since it could simplify endoscopic examination by reducing the number of frames that need physicians review.

The deep learning systems and convolutional neural networks in AI-empowered CE increase its diagnostic yield, allowing the automatic detection and characterization of pathological lesions, with shorter reading time while maintaining high diagnostic accuracy. In the literature the AI-solution has showed a 98% sensitivity and 99% specificity in the identification of occult/obscure gastrointestinal bleeding sources, and an accuracy of 99.9%[3,4]. It should be emphasized that the existing studies are not able to evaluate the bleeding risk of SB lesions, which in clinical practice are fundamental given that decisions about further treatments mostly rely on this factor.

The authors’ proposed model in this retrospective study comprises a two-stage method which explores the image classification and object detection (Improved ResNet50 + YOLO-V5), and can simultaneously detect many SB lesions, providing an accurate localization, and assess the lesions’ bleeding risk. Training data and testing data were constructed respectively on 74574 and 37287 images, subsequently model-assisted readings were compared with physician readings. The diagnosis of at least three specialists was considered as the gold standard in cases where the final diagnosis was unclear or different lesions were identified. The diagnostic performance of this model showed greater sensitivity (99.17%) and accuracy (98.6%) and resulted in significant time-savings compared with standard reading time (48msvs0.4s/image).

Overtime, many models were developed to automatically identify different pathological SB lesions (for example ulcers, polypoid lesions, erosions), however the research on AI in CE is being challenged by their variability in depth, size, forms, and origin. Compared to the existing models[5,6], Zhang et al[ showed a better performance in identification of ulcers, vascular lesions and bleeding.

Last but not least, this study’s remarkable originality was its excellent sensitivity and accuracy in supporting physician reading. The physician’s reading sensitivity was just 52.38%, which meant that almost half of the lesions would have been lost, particularly with SB vascular lesions; this result compared to the 99% sensitivity of the model, bears a huge significance for physicians who are responsible for improving bleeding diagnosis and identifying patients at high risk of bleeding.

We think that this experimental two-stage model could guide next studies (evaluating not only images in a retrospective approach) to better enhance the diagnostic support of AI in the detection of more various SB lesions in clinical practice, especially for young and inexperienced gastroenterologists. Before physicians can fully rely on such solutions and cut down on reading time while preserving (and potentially even increasing) detection accuracy, prospective controlled studies are still mandatory.


Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Gastroenterology and hepatology

Country of origin: Italy

Peer-review report’s classification

Scientific Quality: Grade C

Novelty: Grade B

Creativity or Innovation: Grade B

Scientific Significance: Grade B

P-Reviewer: Ergenç M, TürkiyeS-Editor: Li LL-Editor: AP-Editor: Yuan YY

1.  Zhang RY, Qiang PP, Cai LJ, Li T, Qin Y, Zhang Y, Zhao YQ, Wang JP. Automatic detection of small bowel lesions with different bleeding risks based on deep learning models. World J Gastroenterol. 2024;30:170-183.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (5)]
2.  Rondonotti E, Spada C, Adler S, May A, Despott EJ, Koulaouzidis A, Panter S, Domagk D, Fernandez-Urien I, Rahmi G, Riccioni ME, van Hooft JE, Hassan C, Pennazio M. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Technical Review. Endoscopy. 2018;50:423-446.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 205]  [Cited by in F6Publishing: 231]  [Article Influence: 38.5]  [Reference Citation Analysis (0)]
3.  Soffer S, Klang E, Shimon O, Nachmias N, Eliakim R, Ben-Horin S, Kopylov U, Barash Y. Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. GastrointestEndosc. 2020;92:831-839.e8.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 86]  [Cited by in F6Publishing: 95]  [Article Influence: 23.8]  [Reference Citation Analysis (3)]
4.  Dray X, Iakovidis D, Houdeville C, Jover R, Diamantis D, Histace A, Koulaouzidis A. Artificial intelligence in small bowel capsule endoscopy - current status, challenges and future promise. J Gastroenterol Hepatol. 2021;36:12-19.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 28]  [Cited by in F6Publishing: 36]  [Article Influence: 12.0]  [Reference Citation Analysis (0)]
5.  Aoki T, Yamada A, Kato Y, Saito H, Tsuboi A, Nakada A, Niikura R, Fujishiro M, Oka S, Ishihara S, Matsuda T, Nakahori M, Tanaka S, Koike K, Tada T. Automatic detection of various abnormalities in capsule endoscopy videos by a deep learning-based system: a multicenter study. GastrointestEndosc. 2021;93:165-173.e1.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 24]  [Cited by in F6Publishing: 26]  [Article Influence: 8.7]  [Reference Citation Analysis (0)]
6.  Mascarenhas M, Mendes F, Ribeiro T, Afonso J, Cardoso P, Martins M, Cardoso H, Andrade P, Ferreira J, Mascarenhas Saraiva M, Macedo G. Deep Learning and Minimally Invasive Endoscopy: Automatic Classification of Pleomorphic Gastric Lesions in Capsule Endoscopy. ClinTranslGastroenterol. 2023;14:e00609.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1]  [Cited by in F6Publishing: 2]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]