Synchronized brain dynamics and eye movement trajectory for objective evaluation of robot-assisted surgical skills

同步大脑动力学和眼球运动轨迹，客观评估机器人辅助手术技能

• 批准号: 10569505
• 负责人: Somayeh Besharat Shafiei
• 金额: $ 39.31万
• 依托单位: ROSWELL PARK CANCER INSTITUTE CORP
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10569505
• 关键词: Active Learning; Address; Algorithms; Anastomosis - action; Animals; Applied Skills; Area; Automobile Driving; Behavior; Brain; Categories; Classification; Clinical; Cognitive; Competence; Complex; Data; Development; Dissection; Dryness; Educational Curriculum; Electroencephalography; Engineering; Evaluation; Eye; Eye Movements; Feedback; Goals; Hour; Human; Hysterectomy; Individual; Injury; Knowledge; Learning; Length; Literature; Manuals; Measures; Methodology; Methods; Modality; Modeling; Monitor; Morbidity - disease rate; Needles; Nephrectomy; Neurosciences; Notification; Operating Rooms; Operative Surgical Procedures; Outcome; Pathway Analysis; Patient-Focused Outcomes; Patients; Pattern; Performance; Process; Publishing; Research; Research Proposals; Robot; Robotics; Role; Running; Series; Signal Transduction; Structure; Surgeon; Surgical sutures; System; Tactile; Techniques; Technology; Time; Training; Wrist; adaptive learning; algorithm development; algorithm training; animal tissue; arm; cognitive process; convolutional neural network; cost; deep learning; density; design; gaze; human disease; improved; improved outcome; learning network; learning strategy; motor learning; neural network; neural network algorithm; operation; patient safety; regression algorithm; robot assistance; robotic device; robotic training; safety outcomes; sensor; signal processing; simulation; skill acquisition; skills; surgery outcome; tool

Complicated and costly robot assisted surgery (RAS) training results in less frequent use of this technology in several complex areas of surgery, and consequently ends up in harm. RAS requires a unique blend of skills in addition to manual competence with human-machine interaction skills, while operating remotely from patient with no tactile feedback. To address this challenge, numerous studies have focused on simulation-based robotic training curricula, like Fundamental Skills of Robotic Surgery (FSRS), to develop and assess the performance level of the surgeon operator. However, such training tools were developed based on metrics measured by performance on a simulator and other subjectively evaluated metrics. The goal of this research proposal is to develop a tool for objective RAS skill assessment and a model for performance monitoring. We hypothesize that brain dynamics - Electroencephalogram (EEG) - and eye movement behavior are able to detect change of skill level and the level of surgeon’s performance. To validate this hypothesis, we will record EEG signals and eye movement time series from subjects with different RAS expertise levels. Ten novices, 5 beginners, 5 advanced beginners, and 5 expert surgeons will be included in the study and continuously perform four levels of designed RAS training tasks on surgical robot simulator, dry lab, and animal lab during one year; (1) performing six basic tasks on surgical simulator. All subjects will practice these tasks during two weekly sessions and each practice session takes 2 hours. (2) Subjects will practice 3 tasks of peg transfer, pattern cutting, and suturing on dry lab. (3) Subjects will practice 2 tasks (anastomosis and dissection) on animal tissue and also on plastic models. (4) Subjects will practice two operations of nephrectomy and hysterectomy on animal lab, 2 operations in each session, and each session takes 3 hours and occurs every other week. Two master surgeons will subjectively evaluate performance of subjects (all 25 subjects; Score scale: 1-20) and expertise level (four categories) in performing the designed tasks, every practice session. Master surgeons evaluate surgeon’s skill and performance throughout task and notify change of skill level and performance through time. We will then develop a ‘deep convolutional neural network’ algorithm trained by EEG and eye movement time series through running windows with equal size, to classify subject skill level into four categories of a novice, beginner, advanced beginner, and expert. We will also use network neuroscience techniques to extract engineered features from EEG and eye movement data and use them for training a regression algorithm to develop a model for performance level prediction. Ultimately, the developed objective skill evaluation tool and performance monitoring model will make RAS training more efficient by providing feedback to the trainee regarding his/her skills and directing him/her to focus on skills needed improvement. These improvements will result in more frequent use of RAS in complex surgical areas and ultimately lead to patient safety.

复杂且昂贵的机器人辅助手术（RAS）培训导致该技术的使用频率较低， 几个复杂的手术区域，因此最终受到伤害。RAS需要独特的技能组合， 除了人工能力和人机交互技能之外，还可以从患者处远程操作 没有触觉反馈。为了应对这一挑战，许多研究都集中在基于模拟的 机器人培训课程，如机器人手术的基本技能（FSRS），以开发和评估 外科医生操作员的性能水平。然而，这种培训工具是根据衡量标准开发的 通过模拟器上的性能和其他主观评估指标来衡量。本研究的目的 建议是开发一个客观评估RAS技能的工具和一个绩效模型 监测.我们假设脑动力学-脑电图（EEG）-和眼球运动 行为能够检测技能水平和外科医生表现水平的变化。验证此 假设，我们将记录不同RAS受试者的EEG信号和眼动时间序列 专业水平。将包括10名新手，5名初学者，5名高级初学者和5名专家外科医生， 在手术机器人模拟器上研究并连续执行四个级别设计RAS训练任务， 实验室和动物实验室;（1）在手术模拟器上执行六项基本任务。所有受试者将 每周两次练习这些任务，每次练习2小时。(2)受试者将 练习3项任务：转移钉、切割图案和在干燥实验室中进行切割。(3)受试者将练习2项任务 （吻合和解剖）在动物组织上，也在塑料模型上。(4)受试者将练习两次 在动物实验室进行肾切除术和子宫切除术，每次2例，每次 需要3小时，每隔一周发生一次。两名主刀医生将主观评价 受试者（所有25个受试者;评分标准：1-20）和专业水平（四个类别）在执行设计的 任务，每一次练习。外科大师在整个任务中评估外科医生的技能和表现， 通知技能水平和性能随时间的变化。 然后我们将开发一种通过EEG和眼球运动时间训练的'深度卷积神经网络'算法 系列通过运行大小相等的窗口，将学科技能水平划分为四类新手， 初学者、高级初学者和专家。我们还将使用网络神经科学技术来提取 从EEG和眼球运动数据中设计特征，并将其用于训练回归算法， 建立绩效水平预测模型。最终，开发的客观技能评估工具和 绩效监控模型将通过向受训者提供反馈，使RAS培训更加有效 了解他/她的技能，并指导他/她专注于需要改进的技能。这些改进将 导致RAS在复杂手术区域的使用更加频繁，并最终导致患者安全。