A cascade deep learning model for diagnosing pharyngeal acid reflux episodes using hypopharyngeal multichannel intraluminal Impedance-pH signals

Detecting pharyngeal acid reflux (PAR) episodes from 24-h ambulatory hypopharyngeal multichannel intraluminal impedance-pH (HMII-pH) signals is crucial for diagnosing laryngopharyngeal reflux (LPR). Currently, a lack of effective software for PAR episode detection requires time-consuming manual interpretation, which is prone to inter-rater variability. This study introduces a deep learning-based artificial intelligence (AI) system for PAR episode detection and diagnosis using HMII-pH signals. Ninety patients with suspected LPR and 28 healthy volunteers underwent HMII-pH testing in three Taiwanese medical centers. Candidate PAR episodes were defined as esophagopharyngeal pH drops exceeding 2 units, with nadir pH below 5 within 30 seconds during esophageal acidification. A consensus review by three experts validated 84 PAR episodes in 17 subjects. Data preprocessing identified 225 candidate PAR episodes, including 84 PAR episodes and 141 swallows/artifacts, were divided into training, validation, and test datasets (6:2:2 ratio). Three cascade deep learning AI models were trained. Among them, the cascade Multivariate Long Short-Term Memory with Fully Convolutional Network (MLSTM-FCN) model performed best in the test dataset. At the episode level, this model achieved 0.936 accuracy, 0.941 precision, 0.889 recall, 0.966 specificity, 0.914 F1 score, and 0.864 Matthew's correlation coefficient (MCC). For subject-level evaluation, the corresponding metrics were 0.917 accuracy, 1.000 precision, 0.818 recall, 1.000 specificity, 0.900 F1 score, and 0.842 MCC. In conclusion, the cascade MLSTM-FCN model demonstrates robust accuracy in diagnosing PAR episodes from HMII-pH signals, offering a promising tool for efficient and consistent PAR episode detection in LPR diagnosis.