Topic	Application of machine learning algorithms to predict surgeon experience
Purpose	To differentiate experts (≥100 cases) and novices (<100 cases), as well as super-experts (≥2000 cases) and ordinary-experts (≥100 cases and <2000 cases)
State-of-the-Art	Utilizing automated performance metrics (APMs; robotic kinematic and system events data) on stitch/sub-stitch levels
Knowledge Gaps	Explore the value of detailed APMs during suturing sub-stitch maneuvers in contrast with previous APMs reported over specific steps of a procedure
Technology Gaps	Compare the performance of different machine learning models when presented with datasets of increasing granularity
Future Directions	This is foundational work to provide meaningful feedback to surgeons and learners in training.