Explainable ResNet–long short-term memory model for the classification of bowel sounds frequency based on multifeature fusion

Objective To develop an explainable ResNet–long short-term memory model based on multifeature fusion for classifying bowel sound frequency—a key indicator of gastrointestinal motility. Accurate and objective classification of bowel sound activity levels holds significant clinical value in clinical settings. Methods As a prospective multicenter study conducted across three medical institutions, the primary outcome involved three-way classification of bowel sounds as normoactive, hyperactive, or hypoactive. Bowel sounds were collected and segmented into 10-s clips. Audio features—Chroma, Filter Bank Energies, and Mel-Frequency Cepstral Coefficients—were extracted to train deep learning models using transfer learning with ResNet50 V2, followed by feature fusion and classification via long short-term memory and automated machine learning methods. Results The independent test demonstrated superior performance of the long short-term memory model, achieving an accuracy of 0.927, Matthew’s correlation coefficient of 0.885, and weighted Cohen’s kappa of 0.930—outperforming both automated machine learning models and gastroenterologists. Additionally, the model was evaluated in two clinical scenarios: (a) feeding timing after general anesthesia and (b) bowel preparation for colonoscopy, showing high sensitivity and specificity. Local interpretable model-agnostic explanations were used to enhance model transparency. Conclusions This framework offers a novel, accurate, and explainable approach for bowel sound classification, demonstrating strong potential for clinical applications in gastrointestinal function assessment.