Why the study?
Does an associative memory classifier-based machine-learning algorithm improve diagnostic accuracy in differentiating constrictive pericarditis from restrictive cardiomyopathy compared to standard echocardiographic variables?
Population
141 individuals, including 50 patients with constrictive pericarditis, 44 with restrictive cardiomyopathy…
Comparison
Associative memory classifier-based… vs Standard echocardiographic variables and other…
Design
Cohort
Key result
An associative memory classifier achieved an AUC of 96.2% for differentiating constrictive pericarditis from restrictive cardiomyopathy using speckle tracking and echocardiographic variables.
Authors
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May aid noninvasive differentiation of constrictive pericarditis from restrictive cardiomyopathy; leaves open prospective validation before clinical adoption.
Observational (n=141)
Does an associative memory classifier-based machine-learning algorithm improve diagnostic accuracy in differentiating constrictive pericarditis from restrictive cardiomyopathy compared to standard echocardiographic variables?
Absolute Event Rate: 96.2% vs 82.1%
A cognitive machine-learning algorithm using speckle tracking echocardiography data accurately differentiates constrictive pericarditis from restrictive cardiomyopathy, outperforming standard echocardiographic parameters.
Sengupta et al. (2016) conducted an observational in Constrictive pericarditis and restrictive cardiomyopathy (n=141). Associative memory classifier-based machine-learning algorithm vs. Early diastolic mitral annular velocity and left ventricular longitudinal strain was evaluated on Diagnostic area under the receiver operating characteristic curve for differentiating constrictive pericarditis from restrictive cardiomyopathy. An associative memory classifier achieved an AUC of 96.2% for differentiating constrictive pericarditis from restrictive cardiomyopathy using speckle tracking and echocardiographic variables.