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

ObjectiveTo 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.MethodsAs 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.ResultsThe 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.ConclusionsThis framework offers a novel, accurate, and explainable approach for bowel sound classification, demonstrating strong potential for clinical applications in gastrointestinal function assessment.