Author:
Siva Nanda K.,Singh Yashbir,Hathaway Quincy A.,Sengupta Partho P.,Yanamala Naveena
Abstract
AbstractTo provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the “pattern” of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Madani, A., Ong, J. R., Tibrewal, A. & Mofrad, M. R. K. Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. NPJ Digit. Med. 1, 59 (2018).
2. Domingos, J. S., Stebbing, R. V., Leeson, P. & Noble, J. A. Structured Random Forests for Myocardium Delineation in 3D Echocardiography in Machine Learning in Medical Imaging: 5th International Workshop 215–222 (Springer, 2014).
3. Oktay, O. et al. Multi-input cardiac image super-resolution using convolutional neural networks. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016 (eds Ourselin, S. et al.) 246–254 (Springer, 2016).
4. Voigt, J. U. & Cvijic, M. 2- and 3-Dimensional myocardial strain in cardiac health and disease. JACC Cardiovasc. Imaging 12, 1849–1863 (2019).
5. Singh, Y. et al. Algebraic topology-based machine learning using MRI predicts outcomes in primary sclerosing cholangitis. Eur. Radiol. Exp. 6, 58 (2022).