ArticleSensors (Basel, Switzerland)2021
Analysis of Gastrointestinal Acoustic Activity Using Deep Neural Networks.
Article 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 20 papers, 1 of them a synthesis that pooled it.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review.Neurogastroenterology and motility · 2026Pooled it
- From Fixed-Frequency to Tunable: Advances in Acoustic Sensors for Physiological Acoustic Monitoring.Sensors (Basel, Switzerland) · 2026Review
- Deep learning-based detection of bowel sound events in continuous recordings.Scientific reports · 2026Article
- Clinical Applications and Mathematical Models of Bowel Sounds.Biomedicines · 2026Review
- Review
- 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
- 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
- A Novel You Only Listen Once (YOLO) Deep Learning Model for Automatic Prominent Bowel Sounds Detection: Feasibility Study in Healthy Subjects.Sensors (Basel, Switzerland) · 2025Article
- Machine learning-based diagnostic model for neonatal intestinal diseases in multiple centres: a cross-sectional study protocol.BMJ open · 2025Article
- Dynamic brain-heart-gut coupling during sleep: a continuous physiological signal analysis.Frontiers in neuroscience · 2025Article
- Article
- Article
- [Snoring noise removal method for bowel sound signal during sleep].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Enhancing bowel sound recognition with self-attention and self-supervised pre-training.PloS one · 2024Article
- Differences in intestinal motility during different sleep stages based on long-term bowel sounds.Biomedical engineering online · 2023Article
- Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems.Sensors (Basel, Switzerland) · 2023Review
- Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone.Sensors (Basel, Switzerland) · 2022Article
- Feasibility and basic acoustic characteristics of intelligent long-term bowel sound analysis in term neonates.Frontiers in pediatrics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Automated bowel sound (BS) analysis methods were already well developed by the early 2000s. Accuracy of ~90% had been achieved by several teams using various analytical approaches. Clinical research on BS had revealed their high potential in the non-invasive investigation of irritable bowel syndrome to study gastrointestinal motility and in a surgical setting. This article proposes a novel methodology for the analysis of BS using hybrid convolutional and recursive neural networks. It is one of the first methods of using deep learning to be widely explored. We have developed an experimental pipeline and evaluated our results with a new dataset collected using a device with a dedicated contact microphone. Data have been collected at night-time, which is the most interesting period from a neurogastroenterological point of view. Previous works had ignored this period and instead kept brief records only during the day. Our algorithm can detect bowel sounds with an accuracy >93%. Moreover, we have achieved a very high specificity (>97%), crucial in diagnosis. The results have been checked with a medical professional, and they successfully support clinical diagnosis. We have developed a client-server system allowing medical practitioners to upload the recordings from their patients and have them analyzed online. This system is available online. Although BS research is technologically mature, it still lacks a uniform methodology, an international forum for discussion, and an open platform for data exchange, and therefore it is not commonly used. Our server could provide a starting point for establishing a common framework in BS research.
Indexed as
Identifiers
What Socratic 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 Socratic graph.