ArticleSensors (Basel, Switzerland)2021
Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning.
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 10 papers, 2 of them syntheses that pooled it.
What it found
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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.
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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 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
- Artificial Intelligence for Diagnosis of Esophageal Manometry: A Narrative Review.Current gastroenterology reports · 2026Review
- Development of a diagnostic model for esophageal achalasia assessed by esophageal high-resolution manometry using artificial intelligence.Experimental and therapeutic medicine · 2026Article
- 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
- Emerging innovation, artificial intelligence, and digital technology in esophagology.Annals of the New York Academy of Sciences · 2025Review
- Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images.Medicina (Kaunas, Lithuania) · 2024Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest-the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.
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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.