ReviewSensors (Basel, Switzerland)2021
Automated Bowel Sound Analysis: An Overview.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- An acoustic-only multivariable diagnostic model for Parkinson's disease using bowel sound acoustic signatures.Journal of Parkinson's disease · 2026Article
- A CNN-MAMBA-Based Framework for Salient Bowel Sound Detection and Gastrointestinal Health Assessment.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Review
- Context-aware digital nutrition and abdominal acoustic feedback in Taiwanese university students: a 12-week single-arm field intervention and a separate 4-week randomized feasibility pilot.Frontiers in nutrition · 2026Article
- Behavioral and human-computer interaction requirements for a candidate abdominal-sound-enabled dietary self-regulation system: a two-phase study in Taiwanese university students.Frontiers in nutrition · 2026Article
- Benchmarking machine learning for bowel sound pattern classification - From tabular features to pretrained models.PloS one · 2026Article
- Gastrointestinal emergencies and monitoring in preterm infants: a review.Annals of medicine · 2025Review
- 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
- Explainable ResNet-long short-term memory model for the classification of bowel sounds frequency based on multifeature fusion.The Journal of international medical research · 2025Article
- Machine learning-based diagnostic model for neonatal intestinal diseases in multiple centres: a cross-sectional study protocol.BMJ open · 2025Article
- Article
- Predicting prolonged postoperative ileus in gastric cancer patients based on bowel sounds using intelligent auscultation and machine learning.World journal of gastrointestinal surgery · 2024Article
- SonicGuard Sensor-A Multichannel Acoustic Sensor for Long-Term Monitoring of Abdominal Sounds Examined through a Qualification Study.Sensors (Basel, Switzerland) · 2024Article
- Abdominal physical examinations in early stages benefit critically ill patients without primary gastrointestinal diseases: a retrospective cohort study.Frontiers in medicine · 2024Article
- Enhancing bowel sound recognition with self-attention and self-supervised pre-training.PloS one · 2024Article
- Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring.Nature medicine · 2023Article
- Differences in intestinal motility during different sleep stages based on long-term bowel sounds.Biomedical engineering online · 2023Article
- The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Despite technological progress, we lack a consensus on the method of conducting automated bowel sound (BS) analysis and, consequently, BS tools have not become available to doctors. We aimed to briefly review the literature on BS recording and analysis, with an emphasis on the broad range of analytical approaches. Scientific journals and conference materials were researched with a specific set of terms (Scopus, MEDLINE, IEEE) to find reports on BS. The research articles identified were analyzed in the context of main research directions at a number of centers globally. Automated BS analysis methods were already well developed by the early 2000s. Accuracy of 90% and higher had been achieved with various analytical approaches, including wavelet transformations, multi-layer perceptrons, independent component analysis and autoregressive-moving-average models. Clinical research on BS has exposed their important potential in the non-invasive diagnosis of irritable bowel syndrome, in surgery, and for the investigation of gastrointestinal motility. The most recent advances are linked to the application of artificial intelligence and the development of dedicated BS devices. BS research is technologically mature, but lacks uniform methodology, an international forum for discussion and an open platform for data exchange. A common ground is needed as a starting point. The next key development will be the release of freely available benchmark datasets with labels confirmed by human experts.
Indexed as
Identifiers
What OpenQuestion holds
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.