ArticleVisceral medicine2020
How to Cope with Big Data in Functional Analysis of the Esophagus.
Article in Visceral medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review.Neurogastroenterology and motility · 2026Pooled it
- AI in Esophageal Motility Disorders: Systematic Review of High-Resolution Manometry Studies.Journal of medical Internet research · 2025Pooled it
- Future perspectives in esophageal manometry.World journal of gastrointestinal surgery · 2025Review
- A deep learning-based approach to enhance accuracy and feasibility of long-term high-resolution manometry examinations.Communications medicine · 2025Article
- Additional Diagnostic Yield of Ambulatory 24-h High Resolution Manometry With Impedance in Patients With Non-Cardiac Chest Pain or Non-Obstructive Dysphagia.Neurogastroenterology and motility · 2025Article
- Article
- Artificial Intelligence Tools for Improving Manometric Diagnosis of Esophageal Dysmotility.Current gastroenterology reports · 2024Review
- Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- Artificial Intelligence in Medicine: Passing Hype or the Holy Grail of Solutions?Visceral medicine · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEsophageal motility disorders have a severe impact on patients' quality of life. While high-resolution manometry (HRM) is the gold standard in the diagnosis of esophageal motility disorders, intermittently occurring muscular deficiencies often remain undiscovered if they do not lead to an intense level of discomfort or cause suffering in patients. Ambulatory long-term HRM allows us to study the circadian (dys)function of the esophagus in a unique way. With the prolonged examination period of 24 h, however, there is an immense increase in data which requires personnel and time for evaluation not available in clinical routine. Artificial intelligence (AI) might contribute here by performing an autonomous analysis.
methodsOn the basis of 40 previously performed and manually tagged long-term HRM in patients with suspected temporary esophageal motility disorders, we implemented a supervised machine learning algorithm for automated swallow detection and classification.
resultsFor a set of 24 h of long-term HRM by means of this algorithm, the evaluation time could be reduced from 3 days to a core evaluation time of 11 min for automated swallow detection and clustering plus an additional 10-20 min of evaluation time, depending on the complexity and diversity of motility disorders in the examined patient. In 12.5% of patients with suggested esophageal motility disorders, AI-enabled long-term HRM was able to reveal new and relevant findings for subsequent therapy.
conclusionThis new approach paves the way to the clinical use of long-term HRM in patients with temporary esophageal motility disorders and might serve as an ideal and clinically relevant application of AI.
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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.