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<p>Gastroesophageal reflux disease (GERD) diagnosis traditionally relies on acid exposure time (AET) obtained from 24-h multichannel intraluminal impedance-pH (MII-pH) monitoring, the gold standard for GERD diagnosis. However, a negative result (AET &lt; 4%) does not always exclude GERD, as the limited 24-h monitoring window may fail to capture reflux events in patients with intermittent or low-frequency reflux. To address this limitation, we proposed a complementary machine learning-based framework targeting exclusively patients with negative MII-pH results (AET &lt; 4%) to identify potential false-negative cases within this cohort, by integrating statistical and waveform-derived features from pH signals to enhance anomaly detection. Using one-class support vector machine and support vector data description models trained on real-world MII-pH datasets, the framework achieved an <InlineEquation ID="IEq1"><EquationSource Format="TEX">\({F}_{3}\)</EquationSource><EquationSource Format="MATHML"><math><msub><mrow><mi>F</mi></mrow><mrow><mn>3</mn></mrow></msub></math></EquationSource></InlineEquation> score of approximately 0.9 and identified potential anomalies undetected by the conventional AET criteria. Explainable AI techniques using Shapley additive explanations showed that features such as kurtosis and peak-to-peak amplitude contributed significantly to the identification of subtle reflux patterns within this cohort. These anomalies may indicate additional candidates for clinical reassessment within the AET-negative cohort. This complementary approach, operating downstream of the conventional MII-pH diagnostic system, could help identify potential false-negative cases among patients with negative MII-pH results, potentially assisting in their proper clinical management.</p>

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Reassessing negative 24 h pH impedance tests for hidden gastroesophageal reflux disease using multi feature anomaly detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Songho Lee,
  • Junhyeong Lee,
  • Donggeun Park,
  • Sang Kil Lee,
  • Jae Hee Cho,
  • Kyoung G. Lee,
  • Hee Man Kim,
  • Seunghwa Ryu

摘要

Gastroesophageal reflux disease (GERD) diagnosis traditionally relies on acid exposure time (AET) obtained from 24-h multichannel intraluminal impedance-pH (MII-pH) monitoring, the gold standard for GERD diagnosis. However, a negative result (AET < 4%) does not always exclude GERD, as the limited 24-h monitoring window may fail to capture reflux events in patients with intermittent or low-frequency reflux. To address this limitation, we proposed a complementary machine learning-based framework targeting exclusively patients with negative MII-pH results (AET < 4%) to identify potential false-negative cases within this cohort, by integrating statistical and waveform-derived features from pH signals to enhance anomaly detection. Using one-class support vector machine and support vector data description models trained on real-world MII-pH datasets, the framework achieved an \({F}_{3}\)F3 score of approximately 0.9 and identified potential anomalies undetected by the conventional AET criteria. Explainable AI techniques using Shapley additive explanations showed that features such as kurtosis and peak-to-peak amplitude contributed significantly to the identification of subtle reflux patterns within this cohort. These anomalies may indicate additional candidates for clinical reassessment within the AET-negative cohort. This complementary approach, operating downstream of the conventional MII-pH diagnostic system, could help identify potential false-negative cases among patients with negative MII-pH results, potentially assisting in their proper clinical management.