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人工智能驱动的运动模式的诊断在高分辨率食道测量:一个多中心的多设备研究

Miguel Mascarenhas^1,2,3, Joana Mota^1,2, João Rala Cordeiro^4,5

¹Precision Medicine Unit, Department of Gastroenterology, Centro Hospitalar Universitário São João, Porto, Portugal.

Clinical and translational gastroenterology

|October 23, 2025

概括

机器学习模型使用高分辨率食道测量 (HREM) 数据准确地检测食道运动障碍. 这种人工智能方法提高了这些常见疾病的诊断准确性和一致性.

科学领域:

胃肠病学胃肠病学
医学诊断医学诊断医学诊断
计算生物学计算生物学

背景情况:

食道运动障碍 (EMD) 显著影响患者的生活质量.
高分辨率食道测量仪 (HREM) 是评估EMD的标准.
HREM 分析是复杂的,在可访问性和解释方面带来了挑战.

研究的目的:

开发和验证用于检测EMD的机器学习 (ML) 模型.
使用人工智能标准化HREM解释.
根据芝加哥分类来提高EMD的诊断准确度.

主要方法:

来自多个中心和设备的618次HREM检查的回顾分析.
使用分层患者级数据分割的ML分类器的开发和评估.
通过准确度和接收器操作特征曲线 (AUC-ROC) 下的面积进行性能评估.

主要成果:

梯度增强分类器在食管胃节外流障碍方面实现了0.942准确率和0.921AUC-ROC.
xGBC分类器实现了0.809的准确性和0.871的AUC-ROC对于周周静止障碍.
模型在重复验证中表现出强度和概括性.

结论:

机器学习模型可以从HREM数据中准确地检测EMD.
人工智能驱动的HREM有可能改善诊断标准化.
这种方法可以减少EMD诊断中的观察者间的变异性.

关键词:

人工智能的人工智能是人工智能.食道运动障碍食道运动障碍高分辨率的食道力度计.机器学习是机器学习.

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