Collateral Ventilation Quantification for Endoluminal Valve Placement
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Solution Overview
Problem
Endoluminal valve placement for lung volume reduction is rendered ineffective by high degrees of collateral ventilation, which complicates the evaluation of lung regions and the determination of patient suitability for treatment.
Innovation Solution
A Collateral Ventilation Quantification System (CVQS) combined with machine learning models to assess collateral ventilation, providing indications of its presence and degree, and determining patient candidacy for endoluminal valve placement by analyzing pressure and airflow data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If endoluminal valve placement is performed to reduce lung volume, then healthy lung portions can expand more effectively, but treatment becomes ineffective when collateral ventilation is present
Solution Approach 1:
The system performs preliminary assessment of collateral ventilation using pressure and flow measurements before endoluminal valve placement. By evaluating the degree of collateral ventilation in advance, the system identifies suitable candidates who will benefit from the procedure while excluding those with excessive collateral ventilation that would render the treatment ineffective.
Solution Approach 2:
The system uses feedback from pressure and flow measurements during the assessment procedure to determine the degree of collateral ventilation. This feedback mechanism allows clinicians to objectively evaluate whether a patient is suitable for endoluminal valve placement based on quantified collateral ventilation levels.
2Measurement precision
If collateral ventilation assessment is performed to determine patient suitability, then treatment effectiveness is improved, but evaluation complexity increases
Solution Approach 1:
The system uses the patient's own respiratory system as the test object. By measuring pressure and flow during normal or modified breathing, the system leverages the patient's spontaneous respiratory mechanics to assess collateral ventilation without requiring complex external testing equipment or procedures.
Solution Approach 2:
The system applies pneumatic principles by using pressure and flow measurements through the respiratory system. The assessment utilizes gas flow dynamics and pressure differential measurements to quantify collateral ventilation, employing straightforward pneumatic sensing rather than complex imaging or physiological testing.
Data Source
AI summary
Various aspects of methods, systems, and use cases may be used to train a model to determine whether a patient is a candidate for receiving an endoluminal valve based on collateral ventilation data. A method may include receiving sensor data based on pressure or airflow at a target portion of a lung of a patient that is occluded from receiving air via a breathing airway of the lung. The method may include training a machine learning model, based at least in part on training data (e.g., based on the sensor data), to predict patient breathing outcomes via an indication of whether collateral ventilation is present in a particular patient target lung portion.