ArticleFrontiers in pediatrics2022
Feasibility and basic acoustic characteristics of intelligent long-term bowel sound analysis in term neonates.
Article in Frontiers in pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review.Neurogastroenterology and motility · 2026Pooled it
- Imaging and Non-Imaging Approaches for the Diagnosis and Monitoring of Necrotizing Enterocolitis-What Lies Ahead?Children (Basel, Switzerland) · 2026Review
- 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
- 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
- Diagnostic value of digital continuous bowel sounds in critically ill patients with acute gastrointestinal injury: A prospective observational study.World journal of gastrointestinal surgery · 2024Article
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6 authors.
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Abstract
Objective: Bowel dysfunction continues to be a serious issue in neonates. Traditional auscultation of bowel sounds as a diagnostic tool in neonatal gastrointestinal problems is limited by skill and inability to document and reassess. Consequently, in order to objectively and noninvasively examine the viability of continuous assessment of bowel sounds, we utilized an acoustic recording and analysis system to capture bowel sounds and extract acoustic features in term neonates. Methods: From May 1, 2020 to September 30, 2020, 82 neonates who were hospitalized because of hyperbilirubinemia were included. For 20 h, a convolutional neural network-based acoustic recorder that offers real-time, wireless, continuous auscultation was employed to track the bowel sounds of these neonates. Results: (1) Usable data on five acoustic parameters of bowel sound was recorded for 68 neonates, and the median values were as follows: The rate was 25.80 times/min [interquartile range (IQR): 15.63-36.20]; the duration was 8.00 s/min (IQR: 4.2-13.20); the amplitude was 0.46 (IQR: 0.27-0.68); the frequency was 944.05 Hz (IQR: 848.78-1,034.90); and the interval time was 2.12 s (IQR: 1.3-3.5). (2) In comparison to the parameters of the bowel sounds recorded from the right lower abdomen in 68 infants, the acoustic parameters of the 10 out of 68 infants from chest controls and blank controls were considerably different. (3) The 50%-75% breast milk intake group had the highest rate, the longest duration, and the highest amplitude of bowel sounds, while the >75% breast milk intake group had the highest frequency of bowel sounds. (4) Compared with neonates without hyperbilirubinemia, there was no significant difference in the five parameters of bowel sounds in hyperbilirubinemia infants; nor was there a significant effect of phototherapy and non-phototherapy status on the parameters of bowel sounds during bowel sound monitoring in hyperbilirubinemia patients. (5) A mild transient skin rash appeared on the skin of three infants. No other adverse events occurred. Conclusion: The acoustic recording and analysis system appears useful for monitoring bowel sounds using a continuous, invasive, and real-time approach. Neonatal bowel sounds are affected by various feeding types rather than hyperbilirubinemia and phototherapy. Potential influencing factors and the significance of their application in neonatal intestinal-related disorders require further research.
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