Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging

Abstract

Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.

Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms

“…The use of artificial intelligence can support classical methods by reducing the percentage of overlooked changes [1,10]. AI-assisted endoscopy, which uses facial recognition technologies based on AI, can detect abnormal conditions quickly, based on the analysis of colorectal images, thereby reducing the need for nontumor polypectomy [11]. Furthermore, computer-aided endoscopy has a high degree of accuracy and sensitivity, indicating potential applications in early CRC diagnosis.…”