The Application Value of an Artificial Intelligence-Driven Intestinal Image Recognition Model to Evaluate Intestinal...

Area of Science:

• Gastroenterology
• Medical Imaging
• Artificial Intelligence

Background:

• Colonoscopy quality is crucial for diagnosis but can be subjective.
• Artificial intelligence (AI) offers objective image analysis and high processing capacity.
• AI can potentially standardize colonoscopy by improving bowel preparation assessment.

Purpose of the Study:

• To evaluate the effectiveness of an AI-driven intestinal image recognition model for assessing bowel preparation before colonoscopy.
• To compare AI-based assessment with traditional methods regarding colonoscopy duration, cleanliness, safety, and patient satisfaction.

Main Methods:

• Retrospective analysis of 98 patients undergoing colonoscopy.
• Comparison between an AI group (51 patients) and a Regular group (47 patients) using traditional assessment.
• Analysis of colonoscopy duration, intestinal cleanliness, adverse reactions, and satisfaction.

Main Results:

• AI group showed shorter colonoscopy duration and higher intestinal cleanliness scores (p < 0.05).
• Lower incidence of adverse reactions in the AI group (3.92% vs 10.64%), though not statistically significant.
• Patient satisfaction rates were comparable between the AI group (96.08%) and the Regular group (82.98%).

Conclusions:

• AI-driven intestinal image recognition improves colonoscopy efficiency and bowel preparation quality.
• AI application leads to shorter procedure times and better intestinal cleanliness.
• AI assessment offers comparable patient satisfaction and safety to traditional methods.