A machine learning algorithm using CMR imaging data and demographics accurately differentiated healthy volunteers, hypertrophic cardiomyopathy, and amyloidosis subtypes (AUCs 1.0, 0.99, and 0.92).
Observational (n=400)
Yes
Does a machine learning algorithm using CMR imaging data accurately differentiate hypertrophic cardiomyopathy, AL amyloidosis, and ATTR amyloidosis?
A machine learning model using CMR imaging data and demographics can accurately identify cardiac amyloidosis and differentiate its subtypes from hypertrophic cardiomyopathy.
Effect estimate: AUC 1.0, 0.99, and 0.92
BACKGROUND: Cardiac amyloidosis is associated with poor outcomes and is caused by the interstitial deposition of misfolded proteins, typically ATTR (transthyretin) or AL (light chains). Although specific therapies during early disease stages exist, the diagnosis is often only established at an advanced stage. Cardiovascular magnetic resonance (CMR) is the gold standard for imaging suspected myocardial disease. However, differentiating cardiac amyloidosis from hypertrophic cardiomyopathy may be challenging, and a reliable method for an image-based classification of amyloidosis subtypes is lacking. This study sought to investigate a CMR machine learning (ML) algorithm to identify and distinguish cardiac amyloidosis. METHODS: This retrospective, multicenter, multivendor feasibility study included consecutive patients diagnosed with hypertrophic cardiomyopathy or AL/ATTR amyloidosis and healthy volunteers. Standard clinical information, semiautomated CMR imaging data, and qualitative CMR features were integrated into a trained ML algorithm. RESULTS: Four hundred participants (95 healthy, 94 hypertrophic cardiomyopathy, 95 AL, and 116 ATTR) from 56 institutions were included (269 men aged 58.5 48.4-69.4 years). A 3-stage ML screening cascade sequentially differentiated healthy volunteers from patients, then hypertrophic cardiomyopathy from amyloidosis, and then AL from ATTR. The ML algorithm resulted in an accurate differentiation at each step (area under the curve, 1.0, 0.99, and 0.92, respectively). After reducing included data to demographics and imaging data alone, the performance remained excellent (area under the curve, 0.99, 0.98, and 0.88, respectively), even after removing late gadolinium enhancement imaging data from the model (area under the curve, 1.0, 0.95, 0.86, respectively). CONCLUSIONS: A trained ML model using semiautomated CMR imaging data and patient demographics can accurately identify cardiac amyloidosis and differentiate subtypes.
Weberling et al. (Wed,) conducted a observational in Hypertrophic cardiomyopathy and cardiac amyloidosis (AL/ATTR) (n=400). Machine learning (ML) algorithm using CMR imaging data was evaluated on Differentiation of healthy volunteers from patients, hypertrophic cardiomyopathy from amyloidosis, and AL from ATTR (AUC 1.0, 0.99, and 0.92). A machine learning algorithm using CMR imaging data and demographics accurately differentiated healthy volunteers, hypertrophic cardiomyopathy, and amyloidosis subtypes (AUCs 1.0, 0.99, and 0.92).
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