The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

• Published in: Health data science Vol. 3; p. 0005
• Main Authors: Zhang, Shuaitong, Mu, Wei, Dong, Di, Wei, Jingwei, Fang, Mengjie, Shao, Lizhi, Zhou, Yu, He, Bingxi, Zhang, Song, Liu, Zhenyu, Liu, Jianhua, Tian, Jie
• Format: Journal Article
• Language: English
• Published: United States AAAS 2023; American Association for the Advancement of Science (AAAS)
• Subjects: Review
• ISSN: 2765-8783; 2097-1095; 2765-8783
• DOI: 10.34133/hds.0005

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.