Development and Validation of a Machine Learning System to Identify Reflux Events in Esophageal 24-Hour pH/Impedance Studies

Abstract

Introduction: Esophageal 24-hour pH/impedance testing is routinely performed to diagnose gastroesophageal reflux disease. Interpretation of these studies is time-intensive for expert physicians and has high inter-reader variability. There are no commercially available machine learning tools to assist with automated identification of reflux events in these studies.

Methods: A machine learning system to identify reflux events in 24-hour pH/impedance studies was developed, which included an initial signal processing step and a machine learning model. Gold-standard reflux events were defined by a group of expert physicians. Performance metrics were computed to compare the machine learning system, current automated detection software (Reflux Reader v6.1), and an expert physician reader.

Results: The study cohort included 45 patients (20/5/20 patients in the training/validation/test sets, respectively). The mean age was 51 (standard deviation 14.5) years, 47% of patients were male, and 78% of studies were performed off proton-pump inhibitor. Comparing the machine learning system vs current automated software vs expert physician reader, area under the curve was 0.87 (95% confidence interval [CI] 0.85-0.89) vs 0.40 (95% CI 0.37-0.42) vs 0.83 (95% CI 0.81-0.86), respectively; sensitivity was 68.7% vs 61.1% vs 79.4%, respectively; and specificity was 80.8% vs 18.6% vs 87.3%, respectively.

Discussion: We trained and validated a novel machine learning system to successfully identify reflux events in 24-hour pH/impedance studies. Our model performance was superior to that of existing software and comparable to that of a human reader. Machine learning tools could significantly improve automated interpretation of pH/impedance studies.