Abstract
Simulator training for image-guided surgical interventions would benefit from intelligent systems that detect the evolution of task performance, and take control of individual speed–precision strategies by providing effective automatic performance feedback. At the earliest training stages, novices frequently focus on getting faster at the task. This may, as shown here, compromise the evolution of their precision scores, sometimes irreparably, if it is not controlled for as early as possible. Artificial intelligence could help make sure that a trainee reaches her/his optimal individual speed–accuracy trade-off by monitoring individual performance criteria, detecting critical trends at any given moment in time, and alerting the trainee as early as necessary when to slow down and focus on precision, or when to focus on getting faster. It is suggested that, for effective benchmarking, individual training statistics of novices are compared with the statistics of an expert surgeon. The speed–accuracy functions of novices trained in a large number of experimental sessions reveal differences in individual speed–precision strategies, and clarify why such strategies should be automatically detected and controlled for before further training on specific surgical task models, or clinical models, may be envisaged. How expert benchmark statistics may be exploited for automatic performance control is explained.
Highlights
Technological development and pressure towards a reduction in time available for learning has radically changed the traditional apprenticeship model of surgical training, where simulation offers the opportunity for repeated practice in safe and controlled environments
Simulator training data relative to time and precision of image-guided pick-and-place task performance were recorded from different study populations, including absolute beginners, novice surgeons without specific experience in image-guided simulator training, and expert surgeons with variable experience in image-guided surgery [2,3,5]
These previous studies were aimed at investigating the effects of different camera views, monitor positions and levels of expertise on simulator skill statistics
Summary
Technological development and pressure towards a reduction in time available for learning has radically changed the traditional apprenticeship model of surgical training, where simulation offers the opportunity for repeated practice in safe and controlled environments. The complexity and reliability of commercially available simulators varies considerably, and selecting an appropriate simulator for surgical skill training is in itself a challenge. Within a surgical curriculum, trainees should have dedicated time for simulation-based training with appropriate performance monitoring through effective feedback systems, as the main advantage of computer simulators for surgical training is the opportunity they afford for independent learning. If the simulator does not provide relevant and truly useful instructional feedback to the user, instructors need to be present to supervise and tutor the trainee. The way feedback is delivered to the operator, and the amount of feedback he/she has to process and integrate, represents an important challenge in the development of automatic systems
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