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

图像引导针干预模拟训练中定量性能评估的支持技术

• 批准号: RGPIN-2022-03919
• 负责人: Fichtinger, Gabor
• 金额: $ 4.01万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750618
• 关键词: Enabling; technologies; quantitative; performance; assessment

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.

在加拿大，数百万人进行超声引导的微创针介入治疗，如注射、活检和各种局部癌症治疗。取代侵入性开放手术，这些手术的主要目标是使用实时超声成像引导将手术针精确导航到目标，同时使周围正常组织免受附带损伤。虽然这些手术在专家医生手中可能看起来很简单，但它们通常是复杂而微妙的“微型手术”，需要掌握困难的技能并遵守复杂的手术工作流程。传统上，新手医生在资深专家医生的监督下学习这些人类干预措施。除了使患者暴露于潜在的错误之外，这种经典的学徒制场景从根本上限制了对技能和能力进行客观评估的机会。必须以切实可行和负担得起的方式，通过经常和客观地评估受训者的进展情况，进行严格的跟踪，以确保医学能力。出于患者安全、效率和学习者舒适度的考虑，多个阶段的实践最好在模拟环境中进行，在模拟环境中，患者被人体模型取代，然后只有技术熟练的学习者才能继续在实际患者护理中进行培训。 在当前的医疗模拟器中，各种物理传感器被附接到医生手中的手术器械，以测量它们的位置、速度和加速度，然后使用这些测量的数值分析来推断受训者相对于专家医生基准的技能水平。然而，使用物理传感器存在根本性的限制：它们会改变受训者的感知和学习过程，也许更重要的是，在没有物理传感器的真实的临床环境中，无法测量受训者如何从模拟过渡到人类。此外，目前的“任务培训师”模拟器无法告知整体程序能力，可以说是最重要的指标，承认受训者的责任，病人护理。我的计划的目标是克服这些障碍，通过开发使能技术，通过使用非侵入式多通道视频流（例如，网络摄像头、超声波）来计算程序能力的定量度量，并向学习者提供建设性的可操作反馈。拟议计划的预期贡献将是开发方法和软件工具，以支持对手术技能和能力进行定量评估的跨学科研究。工程和计算机科学研究为新型医疗培训系统的开发提供了信息，有可能提高从业人员的能力，从而对医疗保健产生积极影响。