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 usefulness of machine learning (ML) for predicting colonoscopy procedure duration has not been examined.
A ML algorithm trained on endoscopic data derived from the Clinical Outcomes Research Initiative database predicted colonoscopy procedure duration more accurately than the currently accepted standard practice and the improvement was greater as the tolerance for error decreased.
The aim of this study was 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 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.
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, and the most important features of the model were: patient age, procedure year, and the degree year of provider year.
The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.
Further study is necessary to examine the implications of the deployment of such a model in a clinical setting, and assess if such models can be used in other gastrointestinal procedures.