Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions.

OBJECTIVE: Currently, there is a worldwide shift toward competency-based medical education. This necessitates the use of automated skills assessment methods during self-guided interventions training. Making assessment methods that are transparent and configurable will allow assessment to be interpreted into instructional feedback. The purpose of this work is to develop and validate skills assessment methods in ultrasound-guided interventions that are transparent and configurable.
METHODS: We implemented a method based upon decision trees and a method based upon fuzzy inference systems for technical skills assessment. Subsequently, we validated these methods for their ability to predict scores of operators on a 25-point global rating scale in ultrasound-guided needle insertions and their ability to provide useful feedback for training.
RESULTS: Decision tree and fuzzy rule-based assessment performed comparably to state-of-the-art assessment methods. They produced median errors (on a 25-point scale) of 1.7 and 1.8 for in-plane insertions and 1.5 and 3.0 for out-of-plane insertions, respectively. In addition, these methods provided feedback that was useful for trainee learning. Decision tree assessment produced feedback with median usefulness 7 out of 7; fuzzy rule-based assessment produced feedback with median usefulness 6 out of 7.
CONCLUSION: Transparent and configurable assessment methods are comparable to the state of the art and, in addition, can provide useful feedback. This demonstrates their value in self-guided interventions training curricula.