Estimating Probability for Esophageal Obstruction: A Diagnostic Decision Support Tool Applying Machine Learning to Functional Lumen Imaging Probe Panometry.

ABSTRACT:

Background/aims

This study aimed to develop a diagnostic tool using machine learning to apply functional luminal imaging probe (FLIP) panometry data to determine the probability of esophagogastric junction (EGJ) obstruction as determined using the Chicago Classification version 4.0 (CCv4.0) and high-resolution manometry (HRM).

Methods

Five hundred and fifty-seven adult patients that completed FLIP and HRM (with a conclusive CCv4.0 assessment of EGJ outflow) and 35 asymptomatic volunteers ("controls") were included. EGJ opening was evaluated with 16-cm FLIP performed during sedated endoscopy via EGJ-distensibility index and maximum EGJ diameter. HRM was classified according to the CCv4.0 as conclusive disorders of EGJ outflow or normal EGJ outflow (timed barium esophagram app

SUBMITTER: Schauer JM 

PROVIDER: S-EPMC9577577 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature