ArticleSensors (Basel, Switzerland)2025
A Novel You Only Listen Once (YOLO) Deep Learning Model for Automatic Prominent Bowel Sounds Detection: Feasibility Study in Healthy Subjects.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- A CNN-MAMBA-Based Framework for Salient Bowel Sound Detection and Gastrointestinal Health Assessment.Sensors (Basel, Switzerland) · 2026Article
- Deep learning-based detection of bowel sound events in continuous recordings.Scientific reports · 2026Article
- Review
- A Novel Unsupervised You Only Listen Once (YOLO) Machine Learning Platform for Automatic Detection and Characterization of Prominent Bowel Sounds Towards Precision Medicine.Bioengineering (Basel, Switzerland) · 2025Article
- Prospects of AI-Powered Bowel Sound Analytics for Diagnosis, Characterization, and Treatment Management of Inflammatory Bowel Disease.Medical sciences (Basel, Switzerland) · 2025Review
- QAMT: An LLM-Based Framework for Quality-Assured Medical Time-Series Data Generation.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.