The present study evaluates a parameter discovery approach based on a lumped parameter model of the cardiovascular system in conjunction with optimization to approximate important cardiac parameters, including simulated left ventricle elastances. Important parameters pertaining to ventricular function were estimated using gradient optimization and synthetically generated measurements. Forward-mode automatic differentiation was used to estimate the cost function-parameter matrices and compared to the common finite differences approach. Synthetic data of healthy and diseased hearts were generated as proxies for noninvasive clinical measurements and used to evaluate the algorithm. Twelve parameters including left ventricle elastances were selected for optimization based on 99% explained variation in mean left ventricle pressure and volume. The hybrid optimization strategy yielded the best overall results compared to 1st order optimization with automatic differentiation and finite difference approaches, with mean absolute percentage errors ranging from 6.67% to 14.14%. Errors in left ventricle elastance estimates for simulated aortic stenosis and mitral regurgitation were smallest when including synthetic measurements for arterial pressure and valvular flow rate at approximately 2% and degraded to roughly 5% when including volume trends as well. However, the latter resulted in better tracking of the left ventricle pressure waveforms and may be considered when the necessary equipment is available.

Statistical Shape Models (SSMs) and Sparse Prediction Models (SPMs) based on regressions between cephalometric measurements were compared against standard practice in virtual surgery planning for reconstruction of mandibular defects. Emphasis was placed on the ability of the models to reproduce clinically relevant metrics. CT scans of 50 men and 50 women were collected and split into training and testing datasets according to an 80:20 ratio. The scans were segmented, and anatomical landmarks were identified. SPMs were constructed based on direct regressions between measurements derived from the anatomical landmarks. SSMs were developed by establishing correspondence between the segmented meshes, performing alignment, and principal component analysis. Anterior and bilateral defects were simulated by removing sections of the mandibles in the testing set. Measurement errors after reconstruction ranged from 1.07˚ to 2.2˚ and 0.66 mm to 2.02 mm for mirroring, from 0.45˚ to 3.67˚ and 0.66 mm to 2.54 mm for the SSMs, and from 1.74˚ to 5.01˚ and 0.64 mm to 2.89 mm for the SPMs. Surface-to-surface errors ranged from 1.01 mm to 1.29 mm and 1.06 mm to 1.33 mm for mirroring and SSMs, respectively. Based on the results, SSMs are recommended for VSP in the absence of normal patient anatomy.

Reliable quantification of pulmonary arterial pressure is essential in the diagnostic and prognostic assessment of a range of cardiovascular pathologies, including rheumatic heart disease, yet an accurate and routinely available method for its quantification remains elusive. This work proposes an approach to infer pulmonary arterial pressure based on scientific machine learning techniques and non-invasive, clinically available measurements. A 0D multicompartment model of the cardiovascular system was optimized using several optimization algorithms subject to forward-mode automatic differentiation. Measurement data were synthesized from known parameters to represent the healthy, mitral regurgitant, aortic stenosed, and combined valvular disease situations with and without pulmonary hypertension. Eleven model parameters were selected for optimization based on 95% explained variation in mean pulmonary arterial pressure. A hybrid Adam and limited-memory Broyden–Fletcher–Goldfarb–Shanno optimizer yielded the best results with input data including valvular flow rates, heart chamber volume changes, and systematic arterial pressure. Mean absolute percentage errors ranged from 1.8% to 3.78% over the simulated test cases. The model was able to capture pressure dynamics under hypertensive conditions with pulmonary arterial systole, diastole, and mean pressure average percentage errors of 1.12%, 2.49%, and 2.14%, respectively. The low errors highlight the potential of the proposed model to determine pulmonary pressure for diseased heart valves and pulmonary hypertensive conditions.

The evaluation and accurate diagnosis of the type and severity of aortic stenosis relies on the precision of medical imaging technology and clinical correlations and the expertise of medical professionals. The application of the clinical correlation to different aortic stenosis morphologies and severities is investigated. The manner in which numerical techniques can be used to simulate the blood flow through pathological aortic valves was analysed and compared to the ground-truth CFD model. Larger pressure gradients are estimated in all severities of rheumatic aortic valves compared to calcific aortic valves. The zero-dimensional morphology-insensitive model underpredicted the transvalvular pressure gradient with the greatest error. The 1D model underestimated the pressure gradient in rheumatic cases and overestimated the pressure gradient in calcific cases. The pressure gradients estimated by the clinical approach depends on the location of the flow vena contracta and is sensitive to the severity and type of valve lesion. Through the analysis of entropy generation within the flow domain, the dominant parameters and regions driving adverse pressure gradients were identified. It is concluded that sudden expansion is the dominant parameter leading to higher pressure gradients in rheumatic heart valves compared to calcific ones.