A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis

Abstract

Aims Pulmonary arterial hypertension (PAH) is a progressive condition with high mortality. Quantitative cardiovascular magnetic resonance (CMR) imaging metrics in PAH target individual cardiac structures and have diagnostic and prognostic utility but are challenging to acquire. The primary aim of this study was to develop and test a tensor-based machine learning approach to holistically identify diagnostic features in PAH using CMR, and secondarily, visualize and interpret key discriminative features associated with PAH. Methods and results Consecutive treatment naive patients with PAH or no evidence of pulmonary hypertension (PH), undergoing CMR and right heart catheterization within 48 h, were identified from the ASPIRE registry. A tensor-based machine learning approach, multilinear subspace learning, was developed and the diagnostic accuracy of this approach was compared with standard CMR measurements. Two hundred and twenty patients were identified: 150 with PAH and 70 with no PH. The diagnostic accuracy of the approach was high as assessed by area under the curve at receiver operating characteristic analysis (P < 0.001): 0.92 for PAH, slightly higher than standard CMR metrics. Moreover, establishing the diagnosis using the approach was less time-consuming, being achieved within 10 s. Learnt features were visualized in feature maps with correspondence to cardiac phases, confirming known and also identifying potentially new diagnostic features in PAH. Conclusion A tensor-based machine learning approach has been developed and applied to CMR. High diagnostic accuracy has been shown for PAH diagnosis and new learnt features were visualized with diagnostic potential.

Neural-Network-Based Diagnosis Using 3-Dimensional Myocardial Architecture and Deformation: Demonstration for the Differentiation of Hypertrophic Cardiomyopathy

“…While deformation is considered central to the diagnosis of cardiomyopathies, Swift et al ( 23 ) explored an alternative approach to perform feature extraction directly from static (single phase) 2D cine CMR images. In this work a tensor-based machine-learned model was developed to evaluate pixel-based features from SAX and 4-chamber images, demonstrating the ability to classify presence vs. absence of pulmonary hypertension with good accuracy.…”

Role of biomarkers in evaluation, treatment and clinical studies of pulmonary arterial hypertension

“…116,117 In addition, a tensorbased machine learning approach has been proposed for automatic extraction of disease features in PAH, with diagnostic and prognostic utility. 118 The value of such approaches require comparison with standard measurements derived by human observers.…”

“…In addition to an updated molecular classification of PVD, the phenomic data generated will be a rich resource to the broad community of heart and lung disease investigators. 118 In future clinical studies of PAH subjects, it is hoped that academic centers and pharmaceutical industry sponsors will continue to collaborate to optimize the use of samples collected for analyses of biomarkers and translational research toward the common goal of improving diagnosis, prognosis, and therapies available for patients with PAH.…”

From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?

“…Geometric morphological features of the ventricles on CMR can also be used in PH prediction. Swift and colleagues demonstrated the potential of a tensor-based ML approach that allows for interrogation of CMR data without manual image segmentation [124]. This novel approach was able to differentiate patients with and without PAH with high accuracy.…”