Machine learning analyses of automated performance metrics during granular sub-stitch phases predict surgeon experience

Automated performance metrics (APMs) objectively measure surgeon performance during a robot-assisted radical prostatectomy (RARP). Machine learning (ML) has shown that APMs, especially during the vesico-urethral anastomosis (VUA) of the RARP, are predictive of long-term outcomes such as continence recovery time. This study focuses on APMs during the VUA, specifically on stitch versus sub-stitch levels, to distinguish surgeon experience. During the VUA, APMs, recorded by a systems data recorder (Intuitive Surgical), were reported for each overall stitch (C(total)) and its individual components: needle handling/targeting (C(1)), needle driving (C(2)), and suture cinching (C(3)) (Figure 1A). These metrics were organized into three datasets (GlobalSet [whole stitch], RowSet [independent sub-stitches], and ColumnSet [associated sub-stitches] (Figure 1B) and applied to three ML models (AdaBoost, Gradient Boosting, and Random Forest) in order to solve two classifications tasks: experts (≥ 100 cases) vs. novices (