基于视频的深度学习的多中心验证,以评估在3维高清直肠测量仪上的排便模式
Zarif Azher1, Brian D Ginnebaugh2, David Justin Levinthal3
1Dartmouth College, Hanover, New Hampshire; California Institute of Technology, Pasadena, California; Cedars Sinai Medical Center, Los Angeles, California.
概括
一个深度学习算法准确地识别了使用3D-HDAM跨多个中心的不协同排便. 这项技术有助于诊断胃肠机动性障碍,并揭示了新型失能症亚型.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 深度学习在诊断胃肠机动性障碍方面显示出前景.
- 三维高清门测量 (3D-HDAM) 提供了详细的见解.
- 验证人工智能对3D-HDAM解释的有效性至关重要.
研究的目的:
- 验证深度学习算法用于3D-HDAM的时空分析.
- 在多中心环境中评估算法的诊断准确性.
- 将AI性能与专家解释进行比较.
主要方法:
- 来自3个医疗保健系统 (2018-2022) 的1214个直肠测量研究.
- 与伦敦共识协议 (专家解释) 相比,深度学习算法的性能.
- 曲线下的面积 (AUC) 通过启动抽样计算;威尔科克森测试可信度得分相关性;高斯混合模型用于特征集群.
主要成果:
- 深度混合学习算法在各个站点实现了高AUC (0.99,0.90,0.79).
- 算法信心评分与专家对模糊性的解释有很好的相关性.
- 确定了两种新的失能模式,可能代表不同的表型.
结论:
- 基于视频的深度学习的三维高清门测量 (3D-HDAM) 对于评估门失能是有效的.
- 这项技术具有临床相关性,有助于诊断.
- 未来的应用可能会扩展到其他运动障碍和治疗评估.
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