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开发一种人工智能工具,用于在内血管腹腔大动脉动脉瘤修复过程中进行手术内指导
Allen Li1, Arshia P Javidan2, Babak Namazi3
1Faculty of Medicine & The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada.
Annals of vascular surgery
|November 1, 2023
概括
人工智能 (AI) 模型可以在内血管动脉瘤修复 (EVAR) 期间识别危险的着陆区域,提高手术安全. 这种深度学习工具有助于在复杂的程序中防止视觉判断中的错误.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 手术技术 手术技术
背景情况:
- 手术错误,特别是在视觉感知,有助于不良事件.
- 深度学习 (AI) 提供了实时手术内指导的潜力.
- 在EVAR中识别不合适的着陆区对于患者安全至关重要.
研究的目的:
- 训练和评估一个深度学习模型,用于在EVAR期间识别"No-Go"着陆区.
- 通过使用关键指标来评估模型与人类注释的性能.
主要方法:
- 在EVAR程序的光镜图像上训练了一个深度学习模型.
- "禁区"被定义为由支架移植覆盖最低的动脉.
- 采用了10倍交叉验证,以IoU和F1得分为主要结果.
主要成果:
- 人工智能模型的F1得分为0.53 (±0.32) 和IOU为0.43 (±0.29).
- 像素精度高于0.97 (±0.002),具有0.99 (±0.001) 的特异性.
- 敏感度和PPV分别为0.51 (±0.34) 和0.62 (±0.34).
结论:
- 在EVAR期间,人工智能在识别低于最佳的支架部署区域方面表现出有效性.
- 未来的研究应该专注于外部验证和预测临床结果.
- 该模型显示,它有望增强EVAR的手术内决策.
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