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Veterans Affairs
San Francisco VA Medical CenterMarket Opportunity:
Left ventricular diastolic dysfunction is a heart condition marked by impaired relaxation and decreased compliance in the left ventricle, which affects how the chamber fills with blood during diastole. Diastolic dysfunction is the mechanism underlying diastolic heart failure, comprising approximately half of the heart failure cases in the United States. Diastolic dysfunction is also common after myocardial infarction, the leading cause of systolic heart failure. The prevalence of diastolic dysfunction is increasing, associated with risk factors such as obesity, aging, coronary artery disease, hypertension, and diabetes mellitus.
The standard for left ventricular diastolic function analysis is the end-diastolic pressure and pressure-volume relationship, but measurement requires invasive catheterization. Current non-invasive measures of diastolic function, such as echocardiography, lack reliability in some patients.
Invention:
VA researchers have developed an approach that leverages advanced computational algorithms and mathematical models to accurately capture the complex mechanical behavior of the human heart. As an example, the method can be applied for inverse finite element-based calculation of left ventricular passive myocardial stiffness as a measure of diastolic function, which may be more quantitative than echocardiography.
The finite element model can include a patient’s actual heart geometry derived from medical imaging, such as computed tomography (CT), magnetic resonance imaging (MRI), or echocardiography. Images may be segmented into 2D contours or 3D surfaces of the outer or cavity boundaries for shape analysis that includes contour or surface axes and curvature. Upon receiving an image, the classifier program first preprocesses and extracts relevant features from the input data, which may include anatomical landmarks, texture patterns, intensity profiles, or shape descriptors. For instance, when analyzing an image of a heart, input parameters may include measures of muscle stiffness, myocardial perfusion, ventricular function, or anatomical dimensions.
Once the input parameters are collected, the classifier program employs a combination of statistical modeling, machine learning techniques, and computational simulations to generate predictive models that correlate these parameters with specific health conditions or pathological states. During the classification process, the classifier program evaluates the input parameters against the trained models and computes the likelihood or probability of various diagnostic outcomes. Based on the computed probabilities and diagnostic thresholds, the classifier program then classifies the results as plausible or not, providing clinicians with actionable insights and decision support for patient management. In the case of the heart image analysis, the classifier program may flag abnormal muscle stiffness values indicative of myocardial infarction, cardiomyopathy, or other cardiac pathologies, prompting further investigation or intervention.