Abstract
INTRODUCTION:Microanastomosis is a complex surgical technique that demands delicate and coordinated hand movement. Advances in machine learning technology have enabled new methods to study human motion. Convolutional neural networks are algorithms specialized in visual tasks that enable discrete object detection and tracking including the hands of a surgeon.METHODS:A machine learning hand detector was developed using a pre-trained convolutional neural network capable of tracking 21 hand landmarks without sensors attached. En-to-side microanastomosis was simulated using 2 mm polyvinyl-alcohol vessels and 10/0 micro-suture and recorded with a video camera capturing hand movement in the surgical field. Participants (n = 4) were expert cerebrovascular surgeons with significant operative experience in cerebral bypass surgery. Calculation of motion of 14 hand joint angles was performed using a vectorization method from landmark detections. Time series analysis was performed to calculate the absolute angular motion for each joint of the hand.RESULTS:Four minutes of simulation for each surgeon yielded a total of 784.294 angular detections (14 hand joints total). For the dominant hand, the mean (SD) absolute angular motion was expert 1: 178.2 rad (98.3); expert 2: 261.8 rad (164.3); expert 3: 449.9 rad (307); expert 4: 380.1 rad (198.1).For the non-dominant hand, the mean (SD) absolute angular motion was expert 1: 132.9 rad (64.6), expert 2: 149.2 rad (72.3); expert 3: 389.2 rad (243); expert 4: 162.6 rad (87.2).CONCLUSIONS:A machine learning hand detector system can calculate hand joint motion during microvascular anastomosis. The absolute angular motion is a quantitative performance metric providing information about hand stability during microanastomosis simulation. Such studies using powerful artificial intelligence detection algorithms may have educational potential in the assessment of complex and delicate surgical skills and supervision of surgical training.