ArticleNeurogastroenterology and motility2022
Deep learning-based artificial intelligence model for identifying swallow types in esophageal high-resolution manometry.
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 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 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
- Multimodal framework for swallow detection in video-fluoroscopic swallow studies using manometric pressure distributions from dysphagic patients.International journal of computer assisted radiology and surgery · 2026Article
- A Multimodal Time Point Labeling Approach for Analyzing Mastication and Swallowing Dynamics.Biosensors · 2026Article
- Development of a diagnostic model for esophageal achalasia assessed by esophageal high-resolution manometry using artificial intelligence.Experimental and therapeutic medicine · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- 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
- Artificial intelligence tools for the assessment and management of dysphagia: protocol for a scoping review.BMJ open · 2025Article
- Emerging innovation, artificial intelligence, and digital technology in esophagology.Annals of the New York Academy of Sciences · 2025Review
- 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
- Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.Auris, nasus, larynx · 2025Review
- Artificial intelligence as a transforming factor in motility disorders-automatic detection of motility patterns in high-resolution anorectal manometry.Scientific reports · 2025Article
- Age-defying swallowing.Frontiers in aging · 2025Review
- Article
- 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
- An artificial intelligence platform provides an accurate interpretation of esophageal motility from Functional Lumen Imaging Probe Panometry studies.Neurogastroenterology and motility · 2023Article
- Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThis study aimed to build and evaluate a deep learning, artificial intelligence (AI) model to automatically classify swallow types based on raw data from esophageal high-resolution manometry (HRM).
methodsHRM studies on patients with no history of esophageal surgery were collected including 1,741 studies with 26,115 swallows labeled by swallow type (normal, hypercontractile, weak-fragmented, failed, and premature) by an expert interpreter per the Chicago Classification. The dataset was stratified and split into train/validation/test datasets for model development. Long short-term memory (LSTM), a type of deep-learning AI model, was trained and evaluated. The overall performance and detailed per-swallow type performance were analyzed. The interpretations of the supine swallows in a single study were further used to generate an overall classification of peristalsis. KEY
resultsThe LSTM model for swallow type yielded accuracies from the train/validation/test datasets of 0.86/0.81/0.83. The model's interpretation for study-level classification of peristalsis yielded accuracy of 0.88 in the test dataset. Among model misclassification, 535/698 (77%) swallows and 25/35 (71%) studies were to adjacent categories, for example, normal to weak or normal to ineffective, respectively. CONCLUSIONS AND INFERENCES: A deep-learning AI model can automatically and accurately identify the Chicago Classification swallow types and peristalsis classification from raw HRM data. While future work to refine this model and incorporate overall manometric diagnoses are needed, this study demonstrates the role that AI will serve in the interpretation and classification of esophageal HRM studies.
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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.