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Home Progress in Medical Devices All issues Issue 2

Application of deep learning in the diagnosis of gastrointestinal diseases

Liying Pang1, Xudong Guo1, Qin Zhang2


1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Medical Engineering Department of Northern Jiangsu People's Hospital, Yangzhou 225001, Jiangsu Province, China.


Address correspondence to: Xudong Guo, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, YangPu District, Shanghai 200093, China. Tel: +86-021-55271115; E-mail: guoxd@usst.edu.cn. Qin Zhang, Medical Engineering Department of Northern Jiangsu People's Hospital, No.98 West Nantong Road, Yangzhou 225001, Jiangsu Province, China. Tel: +86-0514-87373012; E-mail: yzzqin@qq.com.


Highlights

● This review presents the current status and challenges of gastrointestinal diseases.

● This review discusses the application of deep learning in diagnosing gastrointestinal diseases.

● This review explores the future development of deep learning in disease diagnosis.

Abstract

With the rapid development of artificial intelligence, deep learning technology has been widely applied across various fields. In the medical field, deep learning models, by analyzing medical images and clinical data, can automatically detect features of different types of lesions, such as polyps, ulcers, and cancers, thereby assisting physicians in early diagnosis of disease. This review provides an overview of recent progress in applying deep learning for disease diagnosis in various parts of the gastrointestinal tract, including the esophagus, stomach, small intestine, and colon. It also discusses the challenges and potential future directions for deep learning in this field.

Keywords: Deep learning, gastrointestinal endoscopy, convolutional neural network, computer-aided detection

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Pang LP, Guo XD, Zhang Q. Application of deep learning in the diagnosis of gastrointestinal diseases. Prog in Med Devices 2025 Jun; 3 (2): 85-95. doi: 10.61189/072185gbtgzi

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