A multi-modal training environment for colonoscopy with pressure feedback.

BACKGROUND

Colonoscopy is a complex procedure with a 3-5% failure rate even for experts. Mastering endoscopy skills and reducing complication rates extend well beyond the initial training phase for most endoscopists, and continues into their clinical experience. Thus, continuing efforts should focus on optimizing training methods to make them individualized with clear objective learning goals for trainees.

METHODS

A hybrid (physical and computer) colonoscopy training simulator was developed using a novel pressure-sensing sleeve covering the full length of a colonoscope, and a physical colon simulator (Kyoto Kagaku) along with custom-designed training software to visualize the color-mapped 3D pressure profile of the colonoscope during the simulated procedure and provide a visual and quantitative evaluation of the endoscopist's skills post-procedure. A system usability questionnaire and objective evaluation metrics were used to determine the model's effectiveness as a training tool.

RESULTS

Thirty-three participants were enrolled in the study, among which 8 were experts and 25 trainees. The interactive maximum and average pressures applied by the trainees were generally higher than those applied by experts, however, this difference was only statistically significant in the recto-sigmoid region. The mean average pressure applied in the rectum and the rectosigmoid region was 6.5 kPa for the experts compared to 13.7 kPa for the trainees, with a p-value of 0.011. Both groups agreed that the system is easy to understand and use, and would be helpful as a learning aid in training programs for colonoscopy skills.

CONCLUSIONS

The proposed system is expected to enhance the quality of colonoscopy procedures by enabling endoscopists to adopt safer and more efficient navigational skills. The evaluation metrics discussed in this research offer useful insights into the performance of endoscopists, and the ability of trainees to compare their performance against expert benchmarks will enable them to establish personalized objective training goals.