ArticleNeurogastroenterology and motility2022
An automated artifact detection and rejection system for body surface gastric mapping.
Article in Neurogastroenterology and motility, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review.Neurogastroenterology and motility · 2026Pooled it
- Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review.Frontiers in medicine · 2024Pooled it
- Expert Clinical Consensus on Body Surface Gastric Mapping Phenotypes for Gastroduodenal Disorders: 'Auckland Classification' v1.0.Neurogastroenterology and motility · 2026Article
- Patient-centered gastrointestinal function assessment technologies: a paradigm shift from traditional approaches to non-invasive innovations.Frontiers in physiology · 2026Review
- Relationship Between Intragastric Meal Distribution, Gastric Emptying, and Gastric Neuromuscular Dysfunction in Chronic Gastroduodenal Disorders.Neurogastroenterology and motility · 2026Article
- A bimodal longitudinal investigation on changes in sentiments over social media interactions owing to COVID-19 pandemic.Scientific reports · 2025Article
- Predicting Symptomatic Response to Prokinetic Treatment Using Gastric Alimetry.Neurogastroenterology and motility · 2025Article
- Abnormal gastric electrophysiology following laparoscopic sleeve gastrectomy and associations with symptoms and quality of life.BJS open · 2025Article
- Neuropathic Gastroduodenal Disorders Can Be Diagnosed by Non-Invasive Body Surface Gastric Mapping: A Comparison With Antroduodenal Manometry.Neurogastroenterology and motility · 2025Article
- Current status of functional testing for upper gastrointestinal disorders: state-of-the-art review.Singapore medical journal · 2025Review
- Body Surface Gastric Mapping Delineates Specific Patient Phenotypes in Adolescents With Functional Dyspepsia and Gastroparesis.Neurogastroenterology and motility · 2025Article
- A Standardized Classification Scheme for Gastroduodenal Disorder Evaluation Using the Gastric Alimetry System: Prospective Cohort Study.Gastro hep advances · 2025Article
- Body surface gastric mapping facilitates the management of pediatric disorders of gut-brain interaction.Therapeutic advances in gastroenterology · 2025Review
- Local Myoelectric Sensing During Human Colonic Tissue Perfusion.Diagnostics (Basel, Switzerland) · 2024Article
- Assessment of Gastric Remnant Activity, Symptoms, and Quality of Life Following Gastric Bypass.Obesity surgery · 2024Article
- Effect of menstrual cycle and menopause on human gastric electrophysiology.American journal of physiology. Gastrointestinal and liver physiology · 2024Article
- Gastric Alimetry Expands Patient Phenotyping in Gastroduodenal Disorders Compared with Gastric Emptying Scintigraphy.The American journal of gastroenterology · 2024Article
- Article
- Gastric AlimetryJournal of clinical medicine · 2023Review
- Principles and clinical methods of body surface gastric mapping: Technical review.Neurogastroenterology and motility · 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
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBody surface gastric mapping (BSGM) is a new clinical tool for gastric motility diagnostics, providing high-resolution data on gastric myoelectrical activity. Artifact contamination was a key challenge to reliable test interpretation in traditional electrogastrography. This study aimed to introduce and validate an automated artifact detection and rejection system for clinical BSGM applications.
methodsTen patients with chronic gastric symptoms generated a variety of artifacts according to a standardized protocol (176 recordings) using a commercial BSGM system (Alimetry, New Zealand). An automated artifact detection and rejection algorithm was developed, and its performance was compared with a reference standard comprising consensus labeling by 3 analysis experts, followed by comparison with 6 clinicians (3 untrained and 3 trained in artifact detection). Inter-rater reliability was calculated using Fleiss' kappa. KEY
resultsInter-rater reliability was 0.84 (95% CI:0.77-0.90) among experts, 0.76 (95% CI:0.68-0.83) among untrained clinicians, and 0.71 (95% CI:0.62-0.79) among trained clinicians. The sensitivity and specificity of the algorithm against experts was 96% (95% CI:91%-100%) and 95% (95% CI:90%-99%), respectively, vs 77% (95% CI:68%-85%) and 99% (95% CI:96%-100%) against untrained clinicians, and 97% (95% CI:92%-100%) and 88% (95% CI:82%-94%) against trained clinicians. CONCLUSIONS & INFERENCES: An automated artifact detection and rejection algorithm was developed showing >95% sensitivity and specificity vs expert markers. This algorithm overcomes an important challenge in the clinical translation of BSGM and is now being routinely implemented in patient test interpretations.
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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.