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

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Colorectal cancer (CRC) is a leading cause of cancer death in China.
• Early CRC diagnosis is crucial for improving survival rates and reducing healthcare costs.
• Current diagnostic methods have limitations in detecting early-stage CRC lesions.

Purpose of the Study:

• To review research on deep learning for colorectal cancer (CRC) lesion image analysis and prediction.
• To provide a reference for early CRC diagnosis by integrating advanced technologies.
• To supplement existing research and offer insights for improving CRC cure rates and reducing mortality.

Main Methods:

• Review of deep learning applications in CRC lesion detection.
• Integration of computer technology, 3D modeling, 5G remote technology, endoscopic robotics, and surgical navigation.
• Analysis of image data for prediction of colorectal cancer lesions.

Main Results:

• Deep learning shows promise in analyzing and predicting colorectal cancer (CRC) lesions from medical images.
• Combining AI with advanced technologies offers potential for enhanced early detection.
• The reviewed research provides a foundation for future diagnostic tools.

Conclusions:

• Deep learning-based image analysis is a valuable tool for early colorectal cancer (CRC) diagnosis.
• Integrating computer technology, 3D modeling, 5G, robotics, and navigation can significantly improve diagnostic accuracy.
• Further research in this interdisciplinary area is essential to combat CRC mortality.