V entrilo : From Mobile Bowel Sound to Precision Gastrointestinal Diagnostic

Bowel sounds are a clinically valuable non-invasive indicator of gastrointestinal motility, yet current systems remain tethered to dedicated auscultation hardware, support only coarse event detection, and do not provide actionable diagnostic interpretation. We present Ventrilo, an end-to-end bowel-sound diagnostic system that runs on both electronic stethoscopes and commodity smartphones. First, a cross-device acquisition pipeline couples active noise cancellation with learned spectral transfer, suppressing ambient interference and aligning smartphone-stethoscope frequency responses to within 2 dB. Second, Listen-BowelNet performs joint segmentation and classification of bowel-sound events, achieving 0.984 Event F1 across three clinically defined event types. Third, an LLM-driven diagnostic engine combines clinical-guideline retrieval with structured evidence reasoning to reach 0.934 diagnostic accuracy at a 0.013 false-alarm rate. The engine outputs structured clinical reports covering four GI diagnostic categories with risk-stratified conclusions, evidence traces, and actionable recommendations. We evaluate Ventrilo on 1,678 recordings from 712 subjects across two medical centres and home environments, using five smartphone models and three stethoscope models. In clinical deployments across acute, chronic, and post-discharge settings, Ventrilo produces actionable alerts from 3.15 hours ahead in-clinic to 1.75 days ahead at home, relative to corresponding physician decisions.