增强手腕关节镜:人工智能应用用于使用机器学习识别骨结构
Antoni Orgiu1, Bihes Karkazan1, Stuart Cannell1
1Orthopaedic, Traumatological Surgery Department, Avicenne University Hospital, Paris, France.
Hand surgery & rehabilitation
|May 26, 2024
概括
人工智能现在可以在手腕关节镜检查期间准确地识别手腕骨. 这种人工智能算法显示出改善手术培训和在这个复杂的手术中的表现的前景.
科学领域:
- 整形外科手术 整形外科手术
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 腕关节镜术是一种复杂的外科手术程序,具有的学习曲线.
- 精确识别解剖结构至关重要,但在关节镜检查期间具有挑战性.
- 人工智能 (AI) 为提高外科培训提供了尚未开发的潜力.
研究的目的:
- 开发和评估一种人工智能算法,用于在手腕关节镜检查过程中识别手腕骨解剖.
- 为算法培训和验证创建一个强大的数据集标记的手腕关节透视图像.
主要方法:
- 开发了一个Deeplabv3+分类算法,具有U-Net架构.
- 从20项手术 (10名患者,10名尸体) 中创建了511张标记的手腕关节透镜图像 (4,088张增强) 的数据库.
- 算法性能使用子损失得分进行评估,目标准确率>80%.
主要成果:
- 人工智能算法在手腕骨的识别方面实现了89%的平均子损失得分.
- 该系统证明可靠地检测了各种手骨.
- 不同的手骨之间,表现略有差异.
结论:
- 人工智能可以在手腕透镜中可靠地检测手腕骨,帮助手术可视化.
- 进一步改进算法可以提高所有手骨的准确性.
- 现实世界的应用可能会提高关节镜手腕手术训练和结果.
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