A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.
In: Scientific reports, Jg. 14 (2024-05-09), Heft 1, S. 10672
Online
academicJournal
Zugriff:
To 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.
(© 2024. The Author(s).)
Titel: |
A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.
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Autor/in / Beteiligte Person: | Siva, NK ; Singh, Y ; Hathaway, QA ; Sengupta, PP ; Yanamala, N |
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Zeitschrift: | Scientific reports, Jg. 14 (2024-05-09), Heft 1, S. 10672 |
Veröffentlichung: | London : Nature Publishing Group, copyright 2011-, 2024 |
Medientyp: | academicJournal |
ISSN: | 2045-2322 (electronic) |
DOI: | 10.1038/s41598-024-61201-4 |
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