Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models, and Machine Learning
This work addresses pulmonary hypertension diagnosis for patients by offering a noninvasive alternative to invasive catheterization, though it appears incremental as it builds on existing methods with feature engineering.
The paper tackled noninvasive estimation of mean pulmonary artery pressure (mPAP) for pulmonary hypertension diagnosis by combining MRI, computer models, and machine learning, showing that physics-informed feature engineering improved Gradient Boosting Decision Trees performance, with regression achieving comparable metrics to classification and providing more informative mPAP values for clinicians.
Pulmonary Hypertension (PH) is a severe disease characterized by an elevated pulmonary artery pressure. The gold standard for PH diagnosis is measurement of mean Pulmonary Artery Pressure (mPAP) during an invasive Right Heart Catheterization. In this paper, we investigate noninvasive approach to PH detection utilizing Magnetic Resonance Imaging, Computer Models and Machine Learning. We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP. We compare results of regression (with thresholding of estimated mPAP) and classification and demonstrate that metrics achieved in both experiments are comparable. The predicted mPAP values are more informative to the physicians than the probability of PH returned by classification models. They provide the intuitive explanation of the outcome of the machine learning model (clinicians are accustomed to the mPAP metric, contrary to the PH probability).