A Virtual Coach to Enhance Surgical Training using Human-Centric Modeling and Adaptive Haptic Guidance

使用以人为本的建模和自适应触觉指导来增强手术训练的虚拟教练

• 批准号: 10491714
• 负责人: Ann Majewicz Fey
• 金额: $ 36.09万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-18 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10491714
• 关键词: Algorithms; Area; Artificial tissue; Behavior; Bilateral; Cause of Death; Cessation of life; Clinical; Clinical Trials; Cognitive; Competence; Complex; Coupled; Credentialing; Cues; Data; Development; Devices; Discipline of obstetrics; Education; Educational Curriculum; Effectiveness; Environment; Evaluation; Feedback; Freedom; Funding Mechanisms; Future; Goals; Gynecologic Oncology; Gynecology; Hand; Hospital Costs; Human; Individual; Injury; Intelligence; Intuition; Lead; Learning; Learning Skill; Machine Learning; Measures; Medical Errors; Medical center; Methods; Modeling; Morbidity - disease rate; Motion; Movement; Nature; Observational Study; Operative Surgical Procedures; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Performance; Physiological; Positioning Attribute; Procedures; Provider; Research; Resource-limited setting; Robot; Robotics; Rotation; Safety; Shapes; Standardization; Surgeon; Surgical Error; Surgical Models; Surveys; System; Tactile; Techniques; Technology; Testing; Time; Tissue Model; Training; Transferable Skills; Translations; Tremor; United States; Urinary Diversion; Urology; Validation; Visual; Work; Wrist; base; data-driven model; design; effectiveness evaluation; emotional factor; experience; experimental study; haptic feedback; haptics; human model; improved; kinematics; medical specialties; mortality; new technology; novel; predictive modeling; preference; prevent; public health relevance; recruit; robotic system; robotic training; simulation; skills; success; virtual coach; virtual reality; visual feedback

PROJECT SUMMARY We aim to reduce surgical robotic errors by developing novel technology to coach experienced practitioners by using real-time data-driven predictive models of operator behavior, task difficulty, and expertise levels during complex surgical training tasks. This technology could increase the effectiveness of simulation-based training, particularly for practicing clinicians, as the predictive models will inform the design of adaptive and personalized feedback for the surgeon. Surgical training typically involves didactic learning, skills labs, and practice on live patients. Safety concerns asso- ciated with training on patients has led to significant developments in simulation-based technology; however, existing simulators may lack the ability promote mastery of skills for practicing providers. Improved training is important for both the provider and the patient. An estimated 100,000 death per year occur due to preventable medical errors. In robotic surgeries, the majority of patient injuries can be attributed to inexperience and lack of technical competence of the attending surgeon. These errors could potentially be avoided through personalized and adaptive coaching. In general, robotic systems can sense and adapt to their environment, even act autonomously to complete a task. However, the majority of surgical robots used today are “teleoperated systems". These systems only perform tasks directly commanded by the human operator, possibly with some scaling or tremor cancellation. There is a missed opportunity to leverage the intelligence of robotic systems to sense and interpret the movements of the surgeon and to enable some form of adaptive feedback for personalized coaching. Our prior work in human-centric modeling could hold the key to the technical challenge of integrating intelligent methods into existing surgical robotic training platforms by better understanding the technical strengths and weaknesses of the practicing surgeon in a data-driven manner. The long-term goal of this project is to improve surgical training outcomes by developing a personalized and adaptive surgical robotic coach capable of providing meaningful feedback to the practicing provider to optimize learning and skill transfer. The specific aims of the proposal include: (1) evaluate the ability of human- centric models to characterize surgeon performance using motion and video data, (2) design adaptive haptic or visual guidance cues to provide learners with real-time feedback and to optimize learning, and (3) evaluate the effectiveness of the adaptive technology coach through end-user validation using procedural-specific training models for general surgery, urology, and gynecologic oncology. This project could significantly improve provider training in robotic surgery. The project could also improve provider training for laparoscopic and open surgery as the models used to develop the virtual coach are inherently human-centric and not tied to any specific surgical tasks or surgical platforms. Our team is uniquely positioned to achieve success in this project, bringing together experts in surgical robotics, human-centric modeling, machine learning, and advanced surgical training. We have conducted extensive preliminary studies in areas related to this proposal, supporting feasibility of this project. Our integration with the Simulation Center at UTSW will enable translation of successful outcomes of this project into the surgical training and retraining pipeline.

项目摘要 我们的目标是通过开发新技术来减少手术机器人的错误， 复杂手术期间操作者行为、任务难度和专业水平的实时数据驱动预测模型 训练任务。这项技术可以提高基于模拟的培训的有效性，特别是对于实践 临床医生，因为预测模型将为外科医生提供自适应和个性化反馈的设计。 外科培训通常包括教学式学习、技能实验室和活体患者实践。安全性阿索 与患者培训相关的技术导致了基于模拟的技术的重大发展;然而，现有的 模拟器可能缺乏促进实践提供者掌握技能的能力。改善培训对双方都很重要 提供者和患者。据估计，每年有10万人死于可预防的医疗差错。在机器人 在外科手术中，大多数患者受伤可归因于缺乏经验和缺乏技术能力， 主治外科医生这些错误可以通过个性化和适应性的指导来避免。 一般来说，机器人系统可以感知和适应环境，甚至自主完成任务。 然而，今天使用的大多数手术机器人都是“遥控系统”。这些系统只执行任务 直接由人类操作员命令，可能具有一些缩放或震颤消除。有一个错过 有机会利用机器人系统的智能来感知和解释外科医生的动作， 以实现用于个性化辅导的某种形式的自适应反馈。我们之前在以人为中心的建模方面的工作可以 掌握将智能方法集成到现有手术机器人培训平台的技术挑战的关键 通过以数据驱动的方式更好地了解执业外科医生的技术优势和弱点。 该项目的长期目标是通过开发个性化的 和自适应手术机器人教练，其能够向执业提供者提供有意义的反馈， 优化学习和技能转移。该提案的具体目标包括：（1）评估人类的能力- 使用运动和视频数据表征外科医生表现的中心模型，（2）设计自适应触觉或视觉 引导线索，为学习者提供实时反馈，优化学习;（3）评估有效性 自适应技术教练通过最终用户验证，使用特定于过程的培训模型， 外科、泌尿科和妇科肿瘤科。该项目可以显著改善机器人手术的提供者培训。 该项目还可以改善腹腔镜和开放手术的提供者培训，因为用于开发 虚拟教练本质上以人为本，不受任何特定手术任务或手术平台的约束。 我们的团队是唯一的定位，以实现成功的这个项目，汇集了专家在手术机器人， 以人为中心的建模、机器学习和高级手术培训。我们进行了广泛的初步调查 与本建议相关的领域的研究，支持本项目的可行性。我们与模拟中心的集成 在UTSW将使该项目的成功成果转化为外科培训和再培训管道。