ArticleNeurogastroenterology and motility2021
Achalasia subtypes can be identified with functional luminal imaging probe (FLIP) panometry using a supervised machine learning process.
Article in Neurogastroenterology and motility, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review.Neurogastroenterology and motility · 2026Pooled it
- Clinical characteristics and manometric findings of esophageal achalasia-a systematic review regarding differences among three subtypes.Journal of smooth muscle research = Nihon Heikatsukin Gakkai kikanshi · 2023Pooled it
- Functional Lumen Imaging Probe (EndoFLIP) in Upper Gastrointestinal Disorders: Current Evidence, Clinical Applications, and Future Perspectives.Diseases (Basel, Switzerland) · 2026Review
- Future perspectives in esophageal manometry.World journal of gastrointestinal surgery · 2025Review
- A combined endoscopy and functional lumen imaging probe panometry approach can expedite diagnosis of esophageal motility disorders.Gastrointestinal endoscopy · 2025Article
- Emerging innovation, artificial intelligence, and digital technology in esophagology.Annals of the New York Academy of Sciences · 2025Review
- Clinical Outcomes of Patients with Achalasia Following Pneumatic Dilation Treatment: A Single Center Experience.Journal of clinical medicine · 2025Article
- Functional Lumen Imaging Probe Panometry.Gastrointestinal endoscopy clinics of North America · 2025Review
- Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.World journal of gastroenterology · 2025Review
- A Standardized Approach to Performing and Interpreting Functional Lumen Imaging Probe Panometry for Esophageal Motility Disorders: The Dallas Consensus.Gastroenterology · 2025Article
- Effects of Cholecystokinin on Esophageal Motor Response to Distension in Asymptomatic Volunteers.Journal of neurogastroenterology and motility · 2024Article
- Artificial Intelligence Tools for Improving Manometric Diagnosis of Esophageal Dysmotility.Current gastroenterology reports · 2024Review
- Tailored myotomy for the treatment of type 3 achalasia: Is there a role for the functional lumen imaging probe?Neurogastroenterology and motility · 2023Article
- Modern Achalasia: Diagnosis, Classification, and Treatment.Journal of neurogastroenterology and motility · 2023Review
- An artificial intelligence platform provides an accurate interpretation of esophageal motility from Functional Lumen Imaging Probe Panometry studies.Neurogastroenterology and motility · 2023Article
- Identifying spastic variant of type II achalasia after treatment with high-resolution manometry and FLIP Panometry.Neurogastroenterology and motility · 2023Article
- Type II achalasia with focal elevated pressures: A distinct manometric and clinical sub-group.Neurogastroenterology and motility · 2022Article
- Validation of secondary peristalsis classification using FLIP panometry in 741 subjects undergoing manometry.Neurogastroenterology and motility · 2022Article
- Dysphagia: Novel and Emerging Diagnostic Modalities.Gastroenterology clinics of North America · 2021Review
- Evaluating esophageal motility beyond primary peristalsis: Assessing esophagogastric junction opening mechanics and secondary peristalsis in patients with normal manometry.Neurogastroenterology and motility · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundAchalasia subtypes on high-resolution manometry (HRM) prognosticate treatment response and help direct management plan. We aimed to utilize parameters of distension-induced contractility and pressurization on functional luminal imaging probe (FLIP) panometry and machine learning to predict HRM achalasia subtypes.
methodsOne hundred eighty adult patients with treatment-naïve achalasia defined by HRM per Chicago Classification (40 type I, 99 type II, 41 type III achalasia) who underwent FLIP panometry were included: 140 patients were used as the training cohort and 40 patients as the test cohort. FLIP panometry studies performed with 16-cm FLIP assemblies were retrospectively analyzed to assess distensive pressure and distension-induced esophageal contractility. Correlation analysis, single tree, and random forest were adopted to develop classification trees to identify achalasia subtypes. KEY
resultsIntra-balloon pressure at 60 mL fill volume, and proportions of patients with absent contractile response, repetitive retrograde contractile pattern, occluding contractions, sustained occluding contractions (SOC), contraction-associated pressure changes >10 mm Hg all differed between HRM achalasia subtypes and were used to build the decision tree-based classification model. The model identified spastic (type III) vs non-spastic (types I and II) achalasia with 90% and 78% accuracy in the train and test cohorts, respectively. Achalasia subtypes I, II, and III were identified with 71% and 55% accuracy in the train and test cohorts, respectively. CONCLUSIONS AND INFERENCES: Using a supervised machine learning process, a preliminary model was developed that distinguished type III achalasia from non-spastic achalasia with FLIP panometry. Further refinement of the measurements and more experience (data) may improve its ability for clinically relevant application.
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