Unleashing the Potential of Machine-Based Learning for the Diagnosis of Cardiac Diseases

Circulation: Cardiovascular Imaging

Volumn 9, Issue 6, 2016, Pages

  Nagueh, Sherif F ^a  

^a HOUSTON METHODIST RESEARCH INSTITUTE   (United States)

Author keywords

cardiomyopathy; cardiovascular disease; constrictive pericarditis; diastolic dysfunction cardiomyopathy; Editorials; pericarditis

Indexed keywords

ALGORITHM; ASSOCIATIVE MEMORY; ASSOCIATIVE MEMORY CLASSIFIER; CLASSIFIER; CONSTRICTIVE PERICARDITIS; ECHOCARDIOGRAPHY; EDITORIAL; HEART CATHETERIZATION; HEART DISEASE; HEART FUNCTION; HUMAN; MACHINE LEARNING; PRIORITY JOURNAL; RESTRICTIVE CARDIOMYOPATHY; DIFFERENTIAL DIAGNOSIS;

CARDIOMYOPATHY, RESTRICTIVE; DIAGNOSIS, DIFFERENTIAL; HEART DISEASES; HUMANS; MACHINE LEARNING; PERICARDITIS, CONSTRICTIVE;

EID: 84975822149     PISSN: 19419651     EISSN: 19420080     Source Type: Journal    
DOI: 10.1161/CIRCIMAGING.116.005059     Document Type: Editorial

References (2)

1. Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K, Gall W, Dudley JT. Cognitive machine-learning algorithm for cardiac imaging: A pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging. 2016;9:e004330. doi: 10.1161/CIRCIMAGING.115.004330.
2. Welch TD, Ling LH, Espinosa RE, Anavekar NS, Wiste HJ, Lahr BD, Schaff HV, Oh JK. Echocardiographic diagnosis of constrictive pericarditis: Mayo Clinic criteria. Circ Cardiovasc Imaging. 2014;7:526-534. doi:10.1161/CIRCIMAGING.113.001613.