ArticleNPJ digital medicine2026
Reassessing negative 24 h pH impedance tests for hidden gastroesophageal reflux disease using multi feature anomaly detection.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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