Enabling technologies for quantitative performance assessment in simulated training of image-guided needle interventions

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Ultrasound-guided minimally invasive needle interventions, such as injections, biopsies and a variety of localized cancer therapies, are performed by the millions in Canada. Replacing invasive open surgeries, the primary goals in these procedures are to precisely navigate a surgical needle to the target while sparing surrounding normal tissues from collateral damage, using real-time ultrasound imaging guidance. While these procedures may look straightforward in the hands of expert physicians, they are often complex and delicate "mini surgeries" that require the mastery of difficult skillsets and compliance to complex procedural workflows. Traditionally, novice physicians learn these interventions on humans under the supervision of senior expert physicians. In addition to exposing patients to potential errors, this classic apprenticeship scenario fundamentally limits the opportunity for objective assessment of skill and competence. Competency in medicine must be assured by rigorous tracking through frequent and objective assessment of the trainee's progress, in a manner that is practical and affordable. For reasons of patient safety, efficiency and learner comfort, multiple stages of practice are best done in simulated environments, where the patient is replaced with mannequins, and then only technically proficient learners may continue their training in actual patient care. In current medical simulators, a variety of physical sensors are attached to the surgical instruments in the physician's hands, to measure their position, speed, and acceleration, and then numerical analysis of these measurements is used to infer the trainee's level of skill with respect to expert physician benchmarks. There are, however, fundamental limitations in using physical sensors: they alter the trainee's perception and learning process, and perhaps more importantly, it is impossible to measure how trainees transition from simulation to humans in a real clinical setting where there are no physical sensors. Moreover, current "task trainer" simulators are unable to inform about overall procedural competence, arguably the most important metric for admitting a trainee to the responsibilities of patient care. The objective of my program is to overcome these barriers, by developing enabling technologies that eliminate physical sensors from the training environment by using non-intrusive multichannel video streams (e.g., webcams, ultrasound) to compute quantitative metrics of procedural competence and to provide constructive actionable feedback to learners. The intended contribution of the proposed program will be the development of methods and software tools in support of cross-disciplinary research on quantitative assessment of surgical skill and competence. Engineering and computer science research that informs the development of novel medical training systems has the potential to improve the competence of practitioners and, as a result, positively impact healthcare.