Computer versus cardiologist: Is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?

Autor:	Bleijendaal H; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, The Netherlands. Electronic address: h.bleijendaal@amsterdamumc.nl., Ramos LA; Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands., Lopes RR; Amsterdam UMC, University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands., Verstraelen TE; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands., Baalman SWE; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands., Oudkerk Pool MD; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands., Tjong FVY; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands., Melgarejo-Meseguer FM; Virgen de Arrixaca Hospital, El Palmar, Spain., Gimeno-Blanes FJ; Miguel Hernández University, Elche, Alicante, Spain., Gimeno-Blanes JR; Virgen de Arrixaca Hospital, El Palmar, Spain; Member of the European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-Heart)., Amin AS; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Member of the European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-Heart)., Winter MM; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands., Marquering HA; Amsterdam UMC, University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands., Kok WEM; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands., Zwinderman AH; Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, The Netherlands., Wilde AAM; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Member of the European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-Heart)., Pinto YM; Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Jazyk:	angličtina
Zdroj:	Heart rhythm [Heart Rhythm] 2021 Jan; Vol. 18 (1), pp. 79-87. Date of Electronic Publication: 2020 Sep 08.
DOI:	10.1016/j.hrthm.2020.08.021
Abstrakt:	Background: Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. Machine learning is a powerful tool used in ECG analysis and has shown to outperform cardiologists. Objectives: We aimed to develop machine learning and deep learning models to diagnose PLN p.Arg14del cardiomyopathy using ECGs and evaluate their accuracy compared to an expert cardiologist. Methods: We included 155 adult PLN mutation carriers and 155 age- and sex-matched control subjects. Twenty-one PLN mutation carriers (13.4%) were classified as symptomatic (symptoms of heart failure or malignant ventricular arrhythmias). The data set was split into training and testing sets using 4-fold cross-validation. Multiple models were developed to discriminate between PLN mutation carriers and control subjects. For comparison, expert cardiologists classified the same data set. The best performing models were validated using an external PLN p.Arg14del mutation carrier data set from Murcia, Spain (n = 50). We applied occlusion maps to visualize the most contributing ECG regions. Results: In terms of specificity, expert cardiologists (0.99) outperformed all models (range 0.53-0.81). In terms of accuracy and sensitivity, experts (0.28 and 0.64) were outperformed by all models (sensitivity range 0.65-0.81). T-wave morphology was most important for classification of PLN p.Arg14del carriers. External validation showed comparable results, with the best model outperforming experts. Conclusion: This study shows that machine learning can outperform experienced cardiologists in the diagnosis of PLN p.Arg14del cardiomyopathy and suggests that the shape of the T wave is of added importance to this diagnosis. (Copyright © 2020 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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