Neurosurg Focus. 2023 Jun;54(6):E2. doi: 10.3171/2023.3.FOCUS2380.
Microanastomosis is one of the most technically demanding and important microsurgical skills for a neurosurgeon. A hand motion detector based on machine learning tracking technology was developed and implemented for performance assessment during microvascular anastomosis simulation.
A microanastomosis motion detector was developed using a machine learning model capable of tracking 21 hand landmarks without physical sensors attached to a surgeon's hands. Anastomosis procedures were simulated using synthetic vessels, and hand motion was recorded with a microscope and external camera. Time series analysis was performed to quantify the economy, amplitude, and flow of motion using data science algorithms. Six operators with various levels of technical expertise (2 experts, 2 intermediates, and 2 novices) were compared.
The detector recorded a mean (SD) of 27.6 (1.8) measurements per landmark per second with a 10% mean loss of tracking for both hands. During 600 seconds of simulation, the 4 nonexperts performed 26 bites in total, with a combined excess of motion of 14.3 (15.5) seconds per bite, whereas the 2 experts performed 33 bites (18 and 15 bites) with a mean (SD) combined excess of motion of 2.8 (2.3) seconds per bite for the dominant hand. In 180 seconds, the experts performed 13 bites, with mean (SD) latencies of 22.2 (4.4) and 23.4 (10.1) seconds, whereas the 2 intermediate operators performed a total of 9 bites with mean (SD) latencies of 31.5 (7.1) and 34.4 (22.1) seconds per bite.
A hand motion detector based on machine learning technology allows the identification of gross and fine movements performed during microanastomosis. Economy, amplitude, and flow of motion were measured using time series data analysis. Technical expertise could be inferred from such quantitative performance analysis.
微血管吻合是神经外科医生最具技术挑战性和重要的显微外科技能之一。我们开发并实施了一种基于机器学习跟踪技术的手动作探测器，用于评估微血管吻合模拟过程中的性能。
我们使用一种能够跟踪 21 个手部标志点的机器学习模型开发了一种微血管吻合运动探测器，该模型无需在外科医生的手上附着物理传感器。使用合成血管模拟吻合过程，并使用显微镜和外部摄像机记录手部运动。使用数据科学算法对手部运动的经济性、幅度和流畅性进行时间序列分析。比较了 6 名具有不同技术水平的操作人员（2 名专家、2 名中级人员和 2 名新手）。
探测器每秒记录每个标志点的平均（标准差）27.6（1.8）个测量值，双手的跟踪丢失率为 10%。在 600 秒的模拟过程中，4 名非专家总共进行了 26 次吻合，每口的总多余动作达 14.3（15.5）秒，而 2 名专家进行了 33 次吻合（18 次和 15 次吻合），在优势手方面，多余动作的平均（标准差）为 2.8（2.3）秒/口。在 180 秒内，专家进行了 13 次吻合，潜伏期的平均值（标准差）分别为 22.2（4.4）和 23.4（10.1）秒，而 2 名中级操作人员总共进行了 9 次吻合，潜伏期的平均值（标准差）分别为 31.5（7.1）和 34.4（22.1）秒/口。
基于机器学习技术的手部运动探测器可以识别微血管吻合过程中的粗略和精细运动。使用时间序列数据分析来测量运动的经济性、幅度和流畅性。可以从这种定量性能分析中推断出技术专长。
