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

Nicolas I Gonzalez-Romo, Sahin Hanalioglu, Giancarlo Mignucci-Jiménez

    Neurosurgical Focus
    |June 7, 2023
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    Summary

    A machine learning hand motion detector assesses microsurgical skills. This technology quantifies motion economy, amplitude, and flow, enabling objective evaluation of neurosurgeon technical expertise during microanastomosis simulation.

    Keywords:
    artificial intelligencecerebral revascularizationhand motion trackingmachine learningmicroanastomosismicroneurosurgerysurgical motion analysis

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    Area of Science:

    • Neurosurgery
    • Microsurgery
    • Medical Simulation
    • Machine Learning
    • Data Science

    Background:

    • Microanastomosis is a critical and complex microsurgical skill for neurosurgeons.
    • Objective performance assessment tools are needed to evaluate and improve technical proficiency.

    Purpose of the Study:

    • To develop and implement a machine learning-based hand motion detector for assessing performance in microanastomosis simulation.
    • To quantify motion economy, amplitude, and flow during simulated procedures.

    Main Methods:

    • A machine learning model tracked 21 hand landmarks without physical sensors.
    • Simulated microanastomosis procedures were recorded using a microscope and external camera.
    • Time series analysis quantified motion parameters, comparing six operators of varying expertise.

    Main Results:

    • The detector achieved high tracking accuracy with minimal data loss.
    • Experts demonstrated significantly greater motion economy and fewer bites compared to non-experts.
    • Latencies were notably shorter in expert operators during simulated anastomosis.

    Conclusions:

    • Machine learning-based hand motion detection effectively identifies gross and fine movements in microanastomosis.
    • Quantitative analysis of motion parameters provides objective insights into technical expertise.
    • This technology can infer and assess a surgeon's skill level during simulated procedures.