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对于内血管外科手术的行动认可框架.

Jochem Bos, Dennis Kundrat, Giulio Dagnino

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    这项研究引入了一个新的AI框架,用于自动识别内血管外科手术的光镜视频中的仪器运动. 这项创新使客观的技能评估成为可能,并推动了机器人手术的发展.

    科学领域:

    • 医疗成像医学成像
    • 在外科手术中使用人工智能
    • 血管内干预 血管内干预

    背景情况:

    • 在内血管外科手术中对仪器操作的客观评估对于技能评估和技术进步至关重要.
    • 目前的方法主要依赖于实验室环境,忽视了有价值的临床光学数据.
    • 需要对临床光学进行自动化分析,以了解外科手术的作用.

    研究的目的:

    • 开发和验证基于学习的框架,用于在临床光镜测序中识别运动活动.
    • 从现实世界手术数据中开创仪器机动的自动识别.
    • 建立基于图像的基础,对内血管程序进行自动评估.

    主要方法:

    • 设计了一个新的深度学习框架,包括两个网络:一个用于仪器细分,另一个用于动作识别.
    • 该框架用于分析来自内血管程序的光学数据.
    • 使用ex vivo数据集证明了可行性.

    主要成果:

    • 拟议的框架成功地证明了在光镜序列中自动识别仪器机动的可行性.
    • 该系统从临床数据中实现了准确的运动活动识别.
    • 初步结果显示了手术行动实时分析的潜力.

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    结论:

    • 开发的框架允许使用光镜数据对内血管任务进行基于图像的自动评估.
    • 这项技术有助于开发用于微创手术的机器人控制系统.
    • 它还支持加强外科教育和智能临床文档.