Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 14, 2024; 30(2): 170-183
Published online Jan 14, 2024. doi: 10.3748/wjg.v30.i2.170
Automatic detection of small bowel lesions with different bleeding risks based on deep learning models
Rui-Ya Zhang, Peng-Peng Qiang, Ling-Jun Cai, Tao Li, Yan Qin, Yu Zhang, Yi-Qing Zhao, Jun-Ping Wang
Rui-Ya Zhang, Ling-Jun Cai, Yan Qin, Yu Zhang, Yi-Qing Zhao, Jun-Ping Wang, Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
Peng-Peng Qiang, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi Province, China
Tao Li, School of Life Sciences and Technology, Mudanjiang Normal University, Mudanjiang 157011, Heilongjiang Province, China
Author contributions: Zhang RY collected the patients’ clinical data and wrote the paper; Wang JP designed the report and revised the paper; Qiang PP and Cai LJ analyzed the data; Qiang PP, Cai LJ, and Li T revised the paper for important intellectual content; Qin Y, Zhang Y, and Zhao YQ collected the patients’ clinical data.
Supported by The Shanxi Provincial Administration of Traditional Chinese Medicine, No. 2023ZYYDA2005.
Institutional review board statement: This study was approved by the Ethics Committee of the Shanxi Provincial People’s Hospital [(2023)No.360].
Informed consent statement: Each patient provided written informed consent for inclusion in the study.
Conflict-of-interest statement: The authors declare having no conflicts of interest.
Data sharing statement: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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:
Corresponding author: Jun-Ping Wang, MD, PhD, Chief Physician, Professor, Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, No. 29 Shuangtasi Street, Taiyuan 030012, Shanxi Province, China.
Received: November 8, 2023
Peer-review started: November 8, 2023
First decision: December 7, 2023
Revised: December 15, 2023
Accepted: December 26, 2023
Article in press: December 26, 2023
Published online: January 14, 2024
Research background

Deep learning provides an efficient automatic image recognition method for small bowel (SB) capsule endoscopy (CE) that can assist physicians in diagnosis. However, the existing deep learning models present some unresolved challenges.

Research motivation

CE reading is time-consuming and complicated. Abnormal parts account for only a small proportion of CE images. Therefore, it is easy to miss the diagnosis, which affects the detection of lesions and assessment of their bleeding risk. Also, both image classification and object detection have made significant progress in the field of deep learning.

Research objectives

To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately, so as to enhance the diagnostic efficiency of physicians and their ability to identify high-risk bleeding groups.

Research methods

The proposed model was a two-stage method that combined image classification with object detection. First, we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images, normal SB mucosa images, and invalid images. Then, the improved YOLO-V5 detection model was utilized to detect the type of lesion and the risk of bleeding, and the location of the lesion was marked. We constructed training and testing sets and compared model-assisted readings with physician readings.

Research results

The accuracy of the model constructed in this study reached 98.96%, which was higher than the accuracy of other systems using only a single module. The sensitivity, specificity, and accuracy of the model-assisted reading detection of all images were 99.17%, 99.92%, and 99.86%, which were significantly higher than those of the endoscopists’ diagnoses. The image processing time of the model was 48 ms/image, and the image processing time of the physicians was 0.40 ± 0.24 s/image (P < 0.001).

Research conclusions

The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images, which enhances the diagnostic efficiency of physicians and improves their ability to identify high-risk bleeding groups.

Research perspectives

We utilized a two-stage combination method and added multiple modules to identify normal SB mucosa images, invalid images, and various SB lesions (lymphangiectasia, lymphoid follicular hyperplasia, xanthoma, erosion, ulcer smaller than 2 cm, protruding lesion smaller than 1 cm, ulcer larger than 2 cm, protruding lesion larger than 1 cm, vascular lesions, and blood). The bleeding risk was evaluated and classified.