Published online Jul 28, 2020. doi: 10.35712/aig.v1.i1.30
Peer-review started: April 23, 2020
First decision: June 4, 2020
Revised: June 15, 2020
Accepted: June 17, 2020
Article in press: June 17, 2020
Published online: July 28, 2020
The use of machine learning (ML) to predict colonoscopy procedure duration has not been examined.
To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estimate procedure duration more accurately than the current practice.
Total 40168 colonoscopies from the Clinical Outcomes Research Initiative database were collected. ML models predicting procedure duration were developed using data available at time of scheduling. The top performing model was compared against historical practice. Models were evaluated based on accuracy (prediction – actual time) ± 5, 10, and 15 min.
ML outperformed historical practice with 77.1% to 68.9%, 87.3% to 79.6%, and 92.1% to 86.8% accuracy at 5, 10 and 15 min thresholds.
The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.
Core tip: Machine learning has been utilized to predict surgical procedure duration and enhance operating room proficiency, however its usefulness for predicting colonoscopy procedure duration has not been examined. Procedure duration predictions from a machine learning algorithm trained on data from the Clinical Outcomes Research Initiative database outperformed historical practice.