Functional luminal imaging probe in the evaluation of esophago-gastric junction outflow obstruction

Ellison, Ashton; Nguyen, Anh D.

doi:10.1097/mog.0000000000000843

Cited by 2 publications

(1 citation statement)

References 53 publications

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“…Typically, the FLIP bag is kept distended at each volume for 60 s before incrementally increasing the bag volume from 30 to 70 mL in steps of 5-10 mL. Under-distension may fail to induce secondary peristalsis or cause dry catheter artifact 35,36 which happens when full occlusion of the FLIP bag interrupts the impedance circuit leading to inaccurate measurements. Over-distension leads to the FLIP bag reaching its infinite compliance limit 6,37 at which point measurements of diameter and pressure are affected by the passive material properties of the FLIP bag rather than the esophagus.…”

Section: Me Thodsmentioning

confidence: 99%

Enhancing Chicago Classification diagnoses with functional lumen imaging probe—mechanics (FLIP‐MECH)

Halder,

Yamasaki,

Liu

et al. 2024

Neurogastroenterology Motil

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BackgroundEsophageal motility disorders can be diagnosed by either high‐resolution manometry (HRM) or the functional lumen imaging probe (FLIP) but there is no systematic approach to synergize the measurements of these modalities or to improve the diagnostic metrics that have been developed to analyze them. This work aimed to devise a formal approach to bridge the gap between diagnoses inferred from HRM and FLIP measurements using deep learning and mechanics.MethodsThe “mechanical health” of the esophagus was analyzed in 740 subjects including a spectrum of motility disorder patients and normal subjects. The mechanical health was quantified through a set of parameters including wall stiffness, active relaxation, and contraction pattern. These parameters were used by a variational autoencoder to generate a parameter space called virtual disease landscape (VDL). Finally, probabilities were assigned to each point (subject) on the VDL through linear discriminant analysis (LDA), which in turn was used to compare with FLIP and HRM diagnoses.ResultsSubjects clustered into different regions of the VDL with their location relative to each other (and normal) defined by the type and severity of dysfunction. The two major categories that separated best on the VDL were subjects with normal esophagogastric junction (EGJ) opening and those with EGJ obstruction. Both HRM and FLIP diagnoses correlated well within these two groups.ConclusionMechanics‐based parameters effectively estimated esophageal health using FLIP measurements to position subjects in a 3‐D VDL that segregated subjects in good alignment with motility diagnoses gleaned from HRM and FLIP studies.

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Section: Me Thodsmentioning

confidence: 99%

Enhancing Chicago Classification diagnoses with functional lumen imaging probe—mechanics (FLIP‐MECH)

Halder,

Yamasaki,

Liu

et al. 2024

Neurogastroenterology Motil

View full text Add to dashboard Cite

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A Software Framework for the Functional Lumen Imaging Probe—Mechanics (MechView)

Halder,

Kou,

Goudie

et al. 2024

Neurogastroenterology Motil

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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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Functional luminal imaging probe in the evaluation of esophago-gastric junction outflow obstruction

Cited by 2 publications

References 53 publications

Enhancing Chicago Classification diagnoses with functional lumen imaging probe—mechanics (FLIP‐MECH)

Enhancing Chicago Classification diagnoses with functional lumen imaging probe—mechanics (FLIP‐MECH)

A Software Framework for the Functional Lumen Imaging Probe—Mechanics (MechView)

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