Metrics for Phonoenterogram Analytics Towards AI Powered Diagnosis of Bowel Diseases: Feasibility Meal Study in a Healthy Subject

With the advances in technology enhancing our ability to record abdominal sounds, the study and analysis of bowel sounds or phonoenterography have drawn significant attention. Clinicians and researchers have attempted to explore the variations in bowel sounds in structural, functional, and neurodegenerative conditions as well as guide the timely initiation of feeds and prediction of complications like paralytic ileus in postsurgical patients. Despite these applications, limited efforts have been made to understand the normal physiology of bowel sounds and their variation with meals. This pilot feasibility study investigates the trends in bowel sounds with different meal compositions at two locations in the abdomen on a healthy subject. The authors also define 4 metrics for phonoenterogram analytics, namely, bowel rate, average bowel rate, average bowel rate variability, and the phonoenterogram (PEG) index. The PEG index was found to be consistent irrespective of the meal constituents or location of the recording. Larger studies are needed to validate and generalize these findings in healthy subjects. Further studies in patients with various gastrointestinal diseases can potentially lead to the development of a novel, noninvasive AI powered diagnostic modality which can transform clinical GI practice.