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

Abstract

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. 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. 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. 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.