A clinical pilot trial of an artificial intelligence-driven smart phone application of bowel preparation for colonoscopy: a randomized clinical trial.

BACKGROUND

High-quality bowel preparation is paramount for a successful colonoscopy. This study aimed to explore the effect of artificial intelligence-driven smartphone software on the quality of bowel preparation.

METHODS

Firstly, we utilized 3305 valid liquid dung images collected via mobile phones as training data. the most effective model was employed on mobile phones to evaluate the quality of bowel preparation. Secondly, From May 2023 to September 2023, colonoscopy patients were randomly assigned to two groups - the AI group (n = 116) and the control group (n = 116) - using a randomized, controlled, endoscopist-blinded method. We compared the two groups in terms of Boston Bowel Preparation Scale (BBPS) scores, polyp detection rate, adverse reaction rate, and factors related to bowel preparation quality. The primary endpoint was the percentage of patients who achieved a BBPS ≥6 among those who effectively utilized the smartphone software.

RESULTS

EfficientNetV2 exhibited the highest performance, with an accuracy of 87%, a sensitivity of 83%, and an AUC of 0.86. In the patient validation experiment, the AI group had higher BBPS scores than the control group (6.78 ± 1.41 vs. 5.35 ± 2.01, p = 0.001) and showed an improvement in the detection rate (71.55% vs. 56.90%, p = 0.020) for polyps. Multifactor logistic analysis indicated that compliance with enema solution usage rules (OR: 5.850, 95% confidence interval: 2.022-16.923), total water intake (OR: 1.001, 95% confidence interval: 1.001-1.002), and AI software reminders (OR: 2.316, 95% confidence interval: 1.096-4.893) were independently associated with BBPS scores ≥6.

CONCLUSION

Compared with traditional methods, the use of artificial intelligence combined with software to send reminders can lead to more accurate assessments of bowel preparation quality and an improved detection rate for polyps, thus demonstrating promising clinical value.