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机器学习算法应用于pH阻抗监测跟踪识别了病理性胃上

Benjamin D Rogers^1,2, Michael Holloway¹, Akinara Sawada³

¹Division of Gastroenterology, Hepatology, and Nutrition, University of Louisville School of Medicine, Louisville, Kentucky, USA.

Neurogastroenterology and motility

|January 19, 2026

概括

机器学习准确地识别了胃上 (SGB),提高了检测能力,超出了手动审查的范围. 这种算法有效地区分SGB患者与患有GERD和健康个体.

科学领域:

胃肠病学胃肠病学
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

胃上吐 (SGB) 很难用当前的软件和手册检查来检测.
准确识别SGB对于患者的诊断和管理至关重要.

研究的目的:

要确定机器学习 (ML) 是否可以准确识别SGB事件.
评估用于SGB检测的ML算法的临床实用性.

主要方法:

一个卷积神经网络 (CNN) 使用来自GERD患者,过度SGB患者和健康志愿者的pH阻抗数据被开发出来.
美国有线电视新闻网 (CNN) 在手动审查的SGB事件上进行了培训,验证和测试,并通过算法识别了新的事件.

主要成果:

该ML算法在将SGB患者与GERD和健康志愿者分离时表现出高准确度 (AUC0.859).
在11个事件的最佳值下,灵敏度为100%,特异性为62.5%.
该算法识别了在手动审查期间错过的独特SGB事件,特别是在SGB队列中.

结论:

一个新的ML算法可以准确地识别SGB事件.
ML方法可靠地将SGB患者与健康个体以及患有GERD的个体区分开来.
这项技术有可能改善SGB诊断和管理.

关键词:

机器学习是机器学习.的pH阻抗.在胃上吐吐.

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