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

Abstract

Background: The 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. Methods: A 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. Results: The 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. Conclusion: The FLIP web framework was developed and validated to reliably estimate various clinical, mechanical, and machine learning-based metrics for diagnosing esophageal motility disorders.