A machine learning-based anomaly detection framework integrating statistical and waveform-derived features achieved an F3 score of approximately 0.9 for identifying potential false-negative gastroesophageal reflux disease cases among patients with normal acid exposure times.
Observational
Does a machine learning-based anomaly detection framework improve the identification of potential false-negative GERD cases in symptomatic patients with negative 24-h MII-pH results?
A machine learning framework using waveform features from 24-h pH monitoring can identify potential false-negative GERD cases in symptomatic patients with normal acid exposure time.
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 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.
Lee et al. (Wed,) conducted a observational in Gastroesophageal reflux disease (GERD). Machine learning-based anomaly detection framework (OCSVM and SVDD) vs. Conventional acid exposure time (AET) criteria was evaluated on F3 score for anomaly detection. A machine learning-based anomaly detection framework integrating statistical and waveform-derived features achieved an F3 score of approximately 0.9 for identifying potential false-negative gastroesophageal reflux disease cases among patients with normal acid exposure times.