Mechanical Ventilator Configuration Using Similar-Patient Lung Models
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Solution Overview
Problem
Current mechanical ventilator settings are determined based on patient body size, which fails to account for the patient-specific mechanical ventilator settings are determined based on patient body size, which fails to account for the patient-specific mechanical ventilator settings are determined based on patient body size, leading to potential Ventilator Induced Lung Injury (VILI) due to lung heterogeneity, especially in conditions like COPD or pneumonia, without the use of patient-specific CT imaging.
Innovation Solution
A method to generate patient-specific mechanical ventilation models using clinical data and a database of CT scans and patient-specific models for similar patients, adjusting settings without requiring new CT scans, utilizing ultrasound or X-ray imaging and machine learning to create a digital twin of the patient's lungs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If patient-specific mechanical ventilator settings are determined based on patient body size, then the ventilation settings can be established using simple measurements, but this approach fails to account for lung heterogeneity leading to potential Ventilator Induced Lung Injury (VILI)
Solution Approach 1:
The system performs preliminary actions by acquiring CT scan data and constructing a 3D biophysical lung model before mechanical ventilation begins. This pre-established digital twin allows virtual testing of ventilator settings to identify safe configuration parameters before actual patient ventilation, preventing VILI while maintaining clinical workflow efficiency.
Solution Approach 2:
The invention creates a digital copy (digital twin) of the patient's lungs using CT scan data and constructs a 3D biophysical model that replicates lung anatomy and mechanical properties. This copy allows virtual simulation and testing of ventilator settings without exposing the actual patient to potential injury, resolving the contradiction between simple configuration and safe ventilation.
2Measurement precision
If a three-dimensional biophysical model of patient's lungs is constructed based on CT exhalation imaging information, then patient-specific ventilation settings can be determined, but this requires additional CT scanning and computational resources
Solution Approach 1:
The 3D biophysical lung model serves multiple functions: it characterizes lung structure and mechanics, enables virtual testing of ventilator settings, and provides a digital twin for ongoing ventilation optimization. This multi-functionality justifies the initial investment in CT imaging and computational modeling, as the same model supports multiple clinical objectives without requiring additional scanning or separate computational systems.
Solution Approach 2:
The 3D biophysical lung model acts as an intermediary between the CT scan data and the mechanical ventilator. Rather than directly translating simple body size measurements into ventilator settings, the model serves as a computational mediator that transforms imaging data into safe ventilation parameters through virtual simulation, bridging the gap between imaging complexity and clinical application.
3Measurement precision
If CT scans are used to construct patient-specific lung models, then accurate digital twins can be created, but this exposes patients to ionizing radiation
Solution Approach 1:
The system performs the CT scan and model construction as a preliminary, one-time action before mechanical ventilation begins. By establishing the digital twin upfront, subsequent ventilation optimization can proceed without requiring additional radiation-exposing scans, thus minimizing total radiation exposure while maintaining model accuracy for ongoing clinical use.
Solution Approach 2:
The invention creates a digital copy of the patient's lungs that can be repeatedly simulated and updated without exposing the patient to additional radiation. Once the initial CT-based digital twin is created, virtual testing and model updates can be performed on the digital copy rather than requiring repeated physical scanning, eliminating ongoing radiation exposure while maintaining measurement precision.
4Ease of operation
If mechanical ventilator settings are based on average or global stress and strain estimates, then configuration is simplified, but local stress and strain concentrations in heterogeneous lung regions are not detected
Solution Approach 1:
The 3D biophysical lung model enables local quality assessment by calculating stress and strain distributions at specific locations within the lung parenchyma. Rather than relying on global averages, the model identifies local heterogeneity and pinpoint s regions of dangerous stress concentrations, allowing clinicians to adjust ventilator settings to protect vulnerable local regions while maintaining overall ventilation effectiveness.
Solution Approach 2:
The digital twin copy allows virtual simulation of local stress and strain distributions without complicating actual ventilator operation. By performing detailed local analysis in the virtual model, the system identifies safe ventilation parameters that can then be applied to the physical ventilator, maintaining ease of operation while achieving precise local stress detection through the digital copy.
Data Source
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AI summary
A non-transitory storage medium stores instructions readable and executable by at least one electronic processor to receive clinical data for a current patient (P); search a database of CT scans and/or patient-specific mechanical ventilation models for other patients using the clinical data for the current patient as a search criterion to identify a similar patient in the database and similar patient data (S) comprising a CT scan and/or a patient-specific mechanical ventilation model for the similar patient; determine a patient-specific mechanical ventilation model for the current patient based on the CT scan and/or patient-specific mechanical ventilation model for the similar patient; generate ventilator configuration data for mechanically ventilating the current patient based on the determined patient-specific mechanical ventilation model for the current patient.