Does fully automatic machine learning-based late gadolinium enhancement analysis improve prediction of major arrhythmic events compared to guideline-based risk criteria in patients evaluated for primary prevention ICD therapy?
AI-based automatic late gadolinium enhancement analysis of cardiac MRI improves the prediction of major arrhythmic events over standard guideline-based criteria for primary prevention ICD implantation.
= .005, respectively). Conclusion In this analysis of the multicenter CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry, fully automatic machine learning-based late gadolinium enhancement analysis reliably quantifies myocardial scar mass and improves the current prediction model that uses guideline-based risk criteria for implantable cardioverter defibrillator implantation. ClinicalTrials.gov registration no.: NCT03352648 Published under a CC BY 4.0 license.
Ghanbari et al. (Tue,) studied this question.