ArticleNeurogastroenterology and motility2025
A Software Framework for the Functional Lumen Imaging Probe-Mechanics (MechView).
Article in Neurogastroenterology and motility, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review.Neurogastroenterology and motility · 2026Pooled it
- Artificial Intelligence and FLIP Panometry-Automated Classification of Esophageal Motility Patterns.Journal of clinical medicine · 2026Article
- Validation of Functional Lumen Imaging Probe Panometry Esophageal Motility Classification Version 2.0: A Study of 805 Patients.Neurogastroenterology and motility · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundThe functional lumen imaging probe (FLIP) has proven to be a versatile device for diagnosing esophageal motility disorders and estimating esophageal wall compliance, but there is a lack of viable software for quantitative assessment of FLIP measurements.
methodsA Python-based web framework was developed for a unified assessment of FLIP measurements including clinical metrics such as esophagogastric junction (EGJ) distensibility index (DI), maximum EGJ opening diameter, mechanics-based metrics for estimating strength, and effectiveness of contractions, such as contraction power and displaced volume, and machine learning-based clustering and predictive algorithms such as the virtual disease landscape (VDL) and EGJ obstruction probability. The clinical and VDL probability metrics were then validated using FLIP data from 121 subjects constituting different categories of EGJ opening which were diagnosed by expert clinicians.
resultsThe clinical metrics estimated by the framework matched the manual diagnosis of the clinicians. Misclassifications were minimal and were mostly between neighboring groups, that is, normal and borderline normal or borderline normal and borderline reduced EGJ opening. Similar results were also obtained for the VDL probability metrics. The misclassifications were further analyzed by clinicians and approved.
conclusionThe FLIP web framework was developed and validated to reliably estimate various clinical, mechanical, and machine learning-based metrics for diagnosing esophageal motility disorders.
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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.