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Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

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

Sourav Halder¹, Wenjun Kou¹, Eric Goudie¹

¹Division of Gastroenterology and Hepatology, Department of Medicine, Feinberg School of Medicine, Kenneth C. Griffin Esophageal Center, Northwestern Medicine, Northwestern University, Chicago, Illinois, USA.

Neurogastroenterology and Motility

|December 14, 2024

Summary

This summary is machine-generated.

A new web framework quantifies functional lumen imaging probe (FLIP) measurements for diagnosing esophageal motility disorders. This tool provides reliable clinical, mechanical, and machine learning metrics, improving diagnostic accuracy.

Keywords:

achalasia dysphagia esophageal biomechanics esophageal motility disorders functional lumen imaging probe

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Area of Science:

Gastroenterology
Medical Devices
Computational Biology

Background:

Functional Lumen Imaging Probe (FLIP) is crucial for diagnosing esophageal motility disorders.
Current limitations exist due to a lack of software for quantitative FLIP data assessment.
Esophageal wall compliance estimation is a key application of FLIP.

Purpose of the Study:

To develop and validate a software framework for comprehensive quantitative assessment of FLIP measurements.
To integrate clinical, mechanical, and machine learning metrics for improved diagnostic capabilities.
To address the need for standardized software in FLIP data analysis.

Main Methods:

A Python-based web framework was created for unified FLIP data assessment.
The framework calculates metrics like esophagogastric junction (EGJ) distensibility index (DI), maximum EGJ opening, contraction power, and displaced volume.
Machine learning algorithms, including virtual disease landscape (VDL) and EGJ obstruction probability, were incorporated and validated on 121 subjects.

Main Results:

The framework's estimated clinical metrics closely aligned with expert clinician diagnoses.
Minimal misclassifications occurred, primarily between adjacent diagnostic categories.
Virtual disease landscape (VDL) probability metrics showed similar accuracy, with validated misclassifications approved by clinicians.

Conclusions:

A validated FLIP web framework reliably estimates diverse metrics for esophageal motility disorder diagnosis.
The software integrates clinical, mechanical, and machine learning approaches for enhanced diagnostic precision.
This framework offers a robust solution for quantitative analysis of FLIP measurements.