Key result
A machine learning algorithm predicted future surgeon performance metrics, significantly outperforming previous task performance on 19 of 36 metric-task pairs (p<0.05).
Why the study?
Pre-operative performance estimation could allow surgeons to proactively improve performance rather than relying solely on post-operative feedback.
Does a machine learning algorithm improve the prediction of surgeon performance on future tasks during robotic-assisted surgery compared to baseline guessing methods?
Does a machine learning algorithm improve the prediction of surgeon performance on future tasks during robotic-assisted surgery compared to baseline guessing methods?
A machine learning algorithm using robotic surgical system data can predict future surgeon performance metrics better than simple baseline guesses, offering a step toward pre-operative performance estimation.
May support preoperative surgeon performance estimation in robotic surgery; leaves open prospective validation before clinical adoption.
We present a machine learning algorithm for predicting surgeon performance on future tasks given performance on previous tasks during robotic-assisted surgery. Performance is estimated via six heuristics, called metrics, which are derived from da Vinci Surgical System data streams and which have previously demonstrated significance in differentiating expertise. We use a boosted ensemble of regression trees, which learns a regression from 193 system data features to one of the six target metrics. Predictions from our algorithm are evaluated on whether they achieve significantly lower error than two intelligent guesses: (1) guessing a surgeon performs the future task exactly as how she performed the previous task (GUESS PREV) and (2) guessing a surgeon will have average performance on the future task (GUESS MEAN). We show that our algorithm can predict metrics of future tasks, significantly (p <; 0.05) outperforming GUESS PREV on 19 out of 36 metrics and task pairs and GUESS MEAN on 8 out of 36 metrics and task pairs. This work serves as a first step towards pre-operative performance estimation, where a surgeon can proactively improve performance rather than relying solely on post-operative feedback.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2019) studied Robotic-assisted minimally invasive surgery. Machine learning algorithm (boosted ensemble of regression trees) vs. GUESS PREV and GUESS MEAN was evaluated on Prediction error of surgeon performance metrics on future tasks (p=<0.05). A machine learning algorithm predicted future surgeon performance metrics, significantly outperforming previous task performance on 19 of 36 metric-task pairs (p<0.05).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: