Hidde Bleijendaal1, Lucas A Ramos2, Ricardo R Lopes3, Tom E Verstraelen4, Sarah W E Baalman4, Marinka D Oudkerk Pool4, Fleur V Y Tjong4, Francisco M Melgarejo-Meseguer5, F Javier Gimeno-Blanes6, Juan R Gimeno-Blanes7, Ahmad S Amin8, Michiel M Winter4, Henk A Marquering3, Wouter E M Kok4, Aeilko H Zwinderman9, Arthur A M Wilde8, Yigal M Pinto4. 1. 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. 2. 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. 3. Amsterdam UMC, University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands. 4. Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands. 5. Virgen de Arrixaca Hospital, El Palmar, Spain. 6. Miguel Hernández University, Elche, Alicante, Spain. 7. 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). 8. 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). 9. Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, The Netherlands.
Abstract
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.
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.Arg14delcardiomyopathy 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.Arg14delcardiomyopathy and suggests that the shape of the T wave is of added importance to this diagnosis.
Authors: R R van de Leur; H Bleijendaal; K Taha; T Mast; J M I H Gho; M Linschoten; B van Rees; M T H M Henkens; S Heymans; N Sturkenboom; R A Tio; J A Offerhaus; W L Bor; M Maarse; H E Haerkens-Arends; M Z H Kolk; A C J van der Lingen; J J Selder; E E Wierda; P F M M van Bergen; M M Winter; A H Zwinderman; P A Doevendans; P van der Harst; Y M Pinto; F W Asselbergs; R van Es; F V Y Tjong Journal: Neth Heart J Date: 2022-03-17 Impact factor: 2.854
Authors: Arjan Sammani; Mark Jansen; Nynke M de Vries; Nicolaas de Jonge; Annette F Baas; Anneline S J M Te Riele; Folkert W Asselbergs; Marish I F J Oerlemans Journal: Front Cardiovasc Med Date: 2022-04-15
Authors: Georgios Petmezas; Leandros Stefanopoulos; Vassilis Kilintzis; Andreas Tzavelis; John A Rogers; Aggelos K Katsaggelos; Nicos Maglaveras Journal: JMIR Med Inform Date: 2022-08-15