Patient-Specific Lung Modeling for Low-Radiation Ventilation Imaging
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Solution Overview
Problem
Current mechanical ventilation systems struggle to prevent Ventilator Induced Lung Injury (VILI) due to the heterogeneity of lung structures and mechanical properties, particularly in patients with conditions like COPD or pneumonia, as they lack patient-specific models that account for local variations and ionizing radiation exposure is a concern.
Innovation Solution
A patient-specific lung model is generated using CT or ultrasound imaging data, allowing for simulation of mechanical ventilation responses and recommending optimal imaging modalities like ultrasound or X-ray based on real-time patient feedback to adjust settings and reduce radiation exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT imaging is used to generate patient-specific lung models, then measurement precision and model accuracy are improved, but ionizing radiation exposure increases
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the patient's lung using CT imaging data, then uses this digital model for repeated simulations and assessments without requiring additional physical CT scans. This allows unlimited virtual testing of ventilation strategies without exposing the patient to further radiation.
Solution Approach 2:
The patent performs comprehensive lung modeling and simulation in advance, creating a detailed digital representation of lung mechanics before actual ventilation therapy begins. This preliminary digital twin can then be used to predict responses to various ventilation strategies, reducing the need for trial-and-error adjustments that would require repeated imaging.
2Ease of operation
If mechanical ventilation settings are optimized for average lung properties, then ease of operation is improved, but manufacturing precision of ventilation therapy decreases
Solution Approach 1:
The patent transitions from uniform ventilation settings based on average lung properties to localized, region-specific ventilation strategies. The digital twin model identifies heterogeneous mechanical properties in different lung regions, allowing clinicians to prescribe tailored ventilation parameters for specific lung zones rather than applying single global settings.
Solution Approach 2:
The system dynamically adjusts ventilation parameters (pressure, volume, flow rates) based on real-time comparison between simulated and actual patient responses. The digital twin allows continuous optimization of ventilation settings by simulating different parameter combinations and predicting their effects on lung mechanics.
3Measurement precision
If frequent imaging is performed to monitor lung conditions, then measurement precision is improved, but loss of time and increased radiation exposure occur
Solution Approach 1:
The patent replaces repeated physical imaging with virtual imaging using the digital twin model. The simulated lung provides a continuous virtual representation of lung mechanics that can be queried instantly without requiring actual CT or radiographic scans, eliminating both radiation exposure and imaging time delays.
Solution Approach 2:
The digital twin enables continuous monitoring of lung conditions through simulation rather than discrete intermittent imaging. The model can predict lung response at any time point based on current ventilation settings and patient physiology, providing uninterrupted assessment without the time gaps between actual imaging studies.
Data Source
AI summary
A mechanical ventilation assessment assistance device includes at least one electronic processor; and a non-transitory storage medium storing instructions readable and executable by the at least one electronic processor to perform a mechanical ventilation assessment assistance method including obtaining an image of a patient (P) receiving mechanical ventilation; generating or updating a patient-specific lung model of at least one lung of the patient based on the obtained image; simulating a response of the patient to a mechanical ventilation therapy using the generated or updated patient-specific lung model; comparing the simulated response with an actual response of the patient to the mechanical ventilation therapy; based on the comparison, determining an imaging recommendation for acquiring an image of at least one lung of the patient; and outputting the determined imaging recommendation.