Quantification of motion during microvascular anastomosis simulation using machine learning hand detection

Abstract

OBJECTIVE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future

“…Mastery of this technique requires extensive training, dedication, and persistence. In light of this, Gonzalez-Romo et al conducted a comprehensive investigation into hand motion during microvascular anastomosis, utilizing a CNN to track 21 hand positions [112]. The study involved six participants, including two experts, two intermediates, and two novices, with no physical constraints imposed on their hand movements.…”

“…Asadi et al demonstrated the superiority of machine learning in predicting outcomes for brain arteriovenous malformations, but limitations include the dependency on large training datasets and the risk of overfitting [111]. Gonzalez-Romo et al developed a machine learning-based hand motion detector for microvascular anastomosis simulation, but limitations include the small sample size and the need for further validation and clinical application [112].…”