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: 10374841
• 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 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10374841
• 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; Educational Curriculum; Electroencephalogram; Engineering; Evaluation; Eye; Eye Movements; Feedback; Goals; Hour; Human; Hysterectomy; Individual; Injury; Knowledge; Lead; Learning; Length; Literature; Manuals; Measures; Methodology; Methods; Modality; Modeling; Monitor; Morbidity - disease rate; Needles; Nephrectomy; Neurosciences; Operating Rooms; Operative Surgical Procedures; Outcome; Pathway Analysis; 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; base; cognitive process; convolutional neural network; cost; deep neural network; density; design; gaze; human disease; improved; learning strategy; motor learning; 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）培训导致该技术在 手术的几个复杂领域，因此最终受到伤害。拉斯需要独特的技能融合 除了人机互动技能的手动能力外，还可以远程操作患者 没有触觉反馈。为了应对这一挑战，大量研究集中于基于模拟的挑战 机器人培训课程，例如机器人手术的基本技能（FSR），以开发和评估 外科医生操作员的性能水平。但是，这种培训工具是根据指标开发的 通过在模拟器和其他主观评估的指标上进行性能衡量。这项研究的目标 建议是为客观RAS技能评估开发工具，并为性能开发模型 监视。我们假设大脑动力学 - 脑电图（EEG）和眼睛运动 行为能够检测技能水平的变化和外科医生的表现水平。验证这一点 假设，我们将记录来自不同RAS受试者的EEG信号和眼动时间序列 专业水平。十本小说，5名初学者，5位高级初学者和5位专家外科医生将包括 该研究并连续执行四个级别的在手术机器人模拟器上设计的RAS训练任务，Dry 实验室和动物实验室一年； （1）在手术模拟器上执行六个基本任务。所有受试者都会 在两个每周的课程中练习这些任务，每个练习时间需要2个小时。 （2）受试者将 练习3个在干燥实验室上的钉钉转移，模式切割和缝合的任务。 （3）受试者将练习2个任务 （吻合和解剖）在动物组织以及塑料模型上。 （4）受试者将练习两个 肾切除术和子宫切除术在动物实验室中的手术，每个会议中有2次手术，每个疗程 需要3个小时，每隔一周就会发生一次。两名大师级外科医生将主观评估 主题（所有25名受试者；得分量表：1-20）和专业知识水平（四个类别）执行设计 任务，每个练习会议。大师级外科医生在整个任务中评估外科医生的技能和表现 随着时间的推移通知技能水平和性能的变化。 然后，我们将开发一种由脑电图和眼动时间训练的“深卷卷神经网络”算法 通过尺寸相等的窗户进行系列，将主题技能水平分为四个小说的四类， 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提取 脑电图和眼动数据的工程功能，并将其用于训练回归算法 为性能级别预测开发模型。最终，开发的客观技能评估工具和 性能监控模型将通过向受训者提供反馈来使RAS培训效率更高 关于他/她的技能，并指示他/她专注于需要提高技能。这些改进将会 导致更频繁地在复杂的手术区域使用RA，并最终导致患者安全。