ArticleArtificial intelligence in medicine2021
A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder.
Article in Artificial intelligence in medicine, 2021. 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, 36 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 in High-resolution Anorectal Manometry: Current Applications and Future Directions.Current gastroenterology reports · 2026Review
- 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
- Article
- A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model.Heliyon · 2024Article
- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- Artificial Intelligence Tools for Improving Manometric Diagnosis of Esophageal Dysmotility.Current gastroenterology reports · 2024Review
- Development of a Deep Learning-Based Epiglottis Obstruction Ratio Calculation System.Sensors (Basel, Switzerland) · 2023Article
- An artificial intelligence platform provides an accurate interpretation of esophageal motility from Functional Lumen Imaging Probe Panometry studies.Neurogastroenterology and motility · 2023Article
- A survey on deep learning applied to medical images: from simple artificial neural networks to generative models.Neural computing & applications · 2023Review
- Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Deep learning-based artificial intelligence model for identifying swallow types in esophageal high-resolution manometry.Neurogastroenterology and motility · 2022Article
- A multi-stage machine learning model for diagnosis of esophageal manometry.Artificial intelligence in medicine · 2022Article
- Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases.Diagnostics (Basel, Switzerland) · 2021Review
- A unified VQ-VAE framework for few-shot retinal vessel segmentation and multidisease classification.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
High-resolution manometry (HRM) is the primary method for diagnosing esophageal motility disorders and its interpretation and classification are based on variables (features) from data of each swallow. Modeling and learning the semantics directly from raw swallow data could not only help automate the feature extraction, but also alleviate the bias from pre-defined features. With more than 32-thousand raw swallow data, a generative model using the approach of variational auto-encoder (VAE) was developed, which, to our knowledge, is the first deep-learning-based unsupervised model on raw esophageal manometry data. The VAE model was reformulated to include different types of loss motivated by domain knowledge and tuned with different hyper-parameters. Training of the VAE model was found sensitive on the learning rate and hence the evidence lower bound objective (ELBO) was further scaled by the data dimension. Case studies showed that the dimensionality of latent space have a big impact on the learned semantics. In particular, cases with 4-dimensional latent variables were found to encode various physiologically meaningful contraction patterns, including strength, propagation pattern as well as sphincter relaxation. Cases with so-called hybrid L2 loss seemed to better capture the coherence of contraction/relaxation transition. Discriminating capability was further evaluated using simple linear discriminative analysis (LDA) on predicting swallow type and swallow pressurization, which yields clustering patterns consistent with clinical impression. The current work on modeling and understanding swallow-level data will guide the development of study-level models for automatic diagnosis as the next stage.
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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.