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相关实验视频

Updated: Jan 9, 2026

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF
08:34

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF

Published on: October 17, 2025

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手术工作流分析:一种可以解释的方法

Christos Spiliadis, Yiheng Chang, Justin Dauwels

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    本研究介绍了一种用于导管实验室外科手术工作流分析的自动化系统,提高了效率和患者安全. 可解释的两阶段模型在实时分类工作流程阶段方面实现了高准确性.

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    科学领域:

    • 医疗成像医学成像
    • 计算机视觉 计算机视觉
    • 医疗保健中的机器学习

    背景情况:

    • 手动手术工作流分析是低效和不一致的.
    • 在导管实验室需要自动化解决方案,以提高效率和患者安全.
    • 现有的方法缺乏解释性和适应性.

    研究的目的:

    • 开发一个可解释的,用于外科手术工作流分析的自动化两阶段模型.
    • 通过使用计算机视觉和机器学习来增强工作流程阶段的实时分类.
    • 验证模型的通用性和临床实施潜力.

    主要方法:

    • 利用天花板上的摄像头进行数据采集.
    • 实现了一个双阶段模型,将YOLOv8物体检测和高斯混合模型 - 隐藏的马尔科夫模型 (GMM-HMM) 结合起来.
    • 用GMM-HMM建模的时空动态来实时工作流阶段分类.

    主要成果:

    • 实现了高准确度:RdGG数据集上的95.2%,HH坦佩雷数据集上的95.4%.
    • 在不同的医院环境中证明了通用性.
    • 突出显示模型的可解释性,稳定性和精确的相位过渡识别.

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    结论:

    • 开发的模型为自动化外科手术工作流分析提供了一个精确,可解释和可适应的解决方案.
    • 由于实时应用和跨医院的适应性,临床实施的潜力.
    • 未来的工作包括提高阻塞强度和整合多模式数据.