Key result
A scientific machine learning approach using a 0-D multicompartment model inferred pulmonary arterial pressure from non-invasive measurements with mean absolute percentage errors of 1.8% to 3.78%.
Why the study?
Reliable quantification of pulmonary arterial pressure is essential across various cardiovascular pathologies, yet an accurate and routinely available non-invasive quantification method remains elusive.
Can a scientific machine learning approach using non-invasive measurements accurately infer pulmonary arterial pressure in simulated healthy and diseased cardiovascular states?
Can a scientific machine learning approach using non-invasive measurements accurately infer pulmonary arterial pressure in simulated healthy and diseased cardiovascular states?
A novel scientific machine learning approach using non-invasive measurements can accurately infer pulmonary arterial pressure in simulated cardiovascular models, offering a potential non-invasive alternative for hemodynamic assessment.
Shows promise for non-invasive PAP estimation in simulation; leaves open prospective patient validation before any practice change.
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 0-D 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 relatively low errors highlight the potential of the proposed model to recover pulmonary pressures for diseased heart valve and pulmonary hypertensive conditions.
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Laubscher et al. (2022) studied Pulmonary hypertension and valvular heart disease. Scientific machine learning and 0-D multicompartment model was evaluated on Mean absolute percentage error of inferred pulmonary arterial pressure. A scientific machine learning approach using a 0-D multicompartment model inferred pulmonary arterial pressure from non-invasive measurements with mean absolute percentage errors of 1.8% to 3.78%.
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