ArticlePloS one2026
Benchmarking machine learning for bowel sound pattern classification - From tabular features to pretrained models.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 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
- Clinical Applications and Mathematical Models of Bowel Sounds.Biomedicines · 2026Review
- Prospects of AI-Powered Bowel Sound Analytics for Diagnosis, Characterization, and Treatment Management of Inflammatory Bowel Disease.Medical sciences (Basel, Switzerland) · 2025Review
- Transformative Impact of Artificial Intelligence on Internal Medicine: Current Applications, Challenges, and Future Horizons for Urban Health.Juntendo medical journal · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of electronic stethoscopes and wearable recording sensors opened the door to the automated analysis of bowel sound (BS) signals. This enables a data-driven analysis of bowel sound patterns, their interrelations, and their correlation with different pathologies. This work leverages a BS dataset collected from 16 healthy subjects that was annotated according to four established BS patterns. This dataset is used to evaluate the performance of machine learning models to detect and/or classify BS patterns. The selection of considered models covers models using tabular features, convolutional neural networks based on spectrograms and models pre-trained on large audio datasets. The results highlight the clear superiority of pre-trained models, particularly in detecting classes with few samples, achieving an AUC of 0.89 in distinguishing BS from non-BS using a HuBERT model and an AUC of 0.89 in differentiating bowel sound patterns using a Wav2Vec 2.0 model. These results pave the way for more comprehensive understanding of bowel sounds in general and future machine-learning-driven diagnostic applications for gastrointestinal examinations.
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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 OpenQuestion graph.