Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis

Hermans, Ben J. M.; Stoks, Job; Bennis, Frank C.; Vink, Arja S.; Garde, Ainara; Wilde, Arthur A.M.; Pison, Laurent; Postema, Pieter G.; Delhaas, Tammo

doi:10.1093/europace/euy243

Cited by 23 publications

(26 citation statements)

References 21 publications

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“…Moreover, as specific T-wave morphologies have been described and verified as viable diagnostic markers for the detection of congenital LQTS [ 4 , 8 , 24 ], Hermans et al [ 25 ] developed a machine learning support vector model showing a high capacity for LQTS detection from genotype negative family members (AUC up to 0.901) by only viewing T-wave morphology without including the complete ECG-waveform.…”

Section: Discussionmentioning

confidence: 99%

Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models

Doldi

Plagwitz

Hoffmann

et al. 2022

JPM

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Introduction: The long-QT syndrome (LQTS) is the most common ion channelopathy, typically presenting with a prolonged QT interval and clinical symptoms such as syncope or sudden cardiac death. Patients may present with a concealed phenotype making the diagnosis challenging. Correctly diagnosing at-risk patients is pivotal to starting early preventive treatment. Objective: Identification of congenital and often concealed LQTS by utilizing novel deep learning network architectures, which are specifically designed for multichannel time series and therefore particularly suitable for ECG data. Design and Results: A retrospective artificial intelligence (AI)-based analysis was performed using a 12-lead ECG of genetically confirmed LQTS (n = 124), including 41 patients with a concealed LQTS (33%), and validated against a control cohort (n = 161 of patients) without known LQTS or without QT-prolonging drug treatment but any other cardiovascular disease. The performance of a fully convolutional network (FCN) used in prior studies was compared with a different, novel convolutional neural network model (XceptionTime). We found that the XceptionTime model was able to achieve a higher balanced accuracy score (91.8%) than the associated FCN metric (83.6%), indicating improved prediction possibilities of novel AI architectures. The predictive accuracy prevailed independently of age and QTc parameters. Conclusions: In this study, the XceptionTime model outperformed the FCN model for LQTS patients with even better results than in prior studies. Even when a patient cohort with cardiovascular comorbidities is used. AI-based ECG analysis is a promising step for correct LQTS patient identification, especially if common diagnostic measures might be misleading.

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Section: Discussionmentioning

confidence: 99%

Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models

Doldi

Plagwitz

Hoffmann

et al. 2022

JPM

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“…9 In addition, although previous studies have identified unique ECG features associated with LQTS or its specific genetic subtypes, these features have mostly involved specific, human-selected features of the ECG, such as shape, slope, and overall morphologic characteristics of the T wave. 2,6,7,14 In a step to involve AI, Hermans and colleagues 15 built upon these T wave morphologic studies by developing a machine learning, support-vector model showing that vectorcardiographic parameters from the T wave can improve diagnosis of LQTS with the capacity to distinguish patients with LQTS from genotypenegative family members with an AUC up to 0.901 based on the model used. In contrast, our AI-ECG used unsupervised feature extraction in which an agnostic approach of the complete ECG waveform across all 12 leads of the ECG, as compared with the T wave alone, was analyzed during training, with network feature selection based on minimizing an error function.…”

Section: Discussionmentioning

confidence: 99%

Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram

Bos

Attia

Albert³

et al. 2021

JAMA Cardiol

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IMPORTANCELong QT syndrome (LQTS) is characterized by prolongation of the QT interval and is associated with an increased risk of sudden cardiac death. However, although QT interval prolongation is the hallmark feature of LQTS, approximately 40% of patients with genetically confirmed LQTS have a normal corrected QT (QTc) at rest. Distinguishing patients with LQTS from those with a normal QTc is important to correctly diagnose disease, implement simple LQTS preventive measures, and initiate prophylactic therapy if necessary.OBJECTIVE To determine whether artificial intelligence (AI) using deep neural networks is better than the QTc alone in distinguishing patients with concealed LQTS from those with a normal QTc using a 12-lead electrocardiogram (ECG). DESIGN, SETTING, AND PARTICIPANTSA diagnostic case-control study was performed using all available 12-lead ECGs from 2059 patients presenting to a specialized genetic heart rhythm clinic. Patients were included if they had a definitive clinical and/or genetic diagnosis of type 1, 2, or 3 LQTS (LQT1, 2, or 3) or were seen because of an initial suspicion for LQTS but were discharged without this diagnosis. A multilayer convolutional neural network was used to classify patients based on a 10-second, 12-lead ECG, AI-enhanced ECG (AI-ECG). The convolutional neural network was trained using 60% of the patients, validated in 10% of the patients, and tested on the remaining patients (30%). The study was conducted from January 1, 1999, to December 31, 2018. MAIN OUTCOMES AND MEASURESThe goal of the study was to test the ability of the convolutional neural network to distinguish patients with LQTS from those who were evaluated for LQTS but discharged without this diagnosis, especially among patients with genetically confirmed LQTS but a normal QTc value at rest (referred to as genotype positive/phenotype negative LQTS, normal QT interval LQTS, or concealed LQTS). RESULTSOf the 2059 patients included, 1180 were men (57%); mean (SD) age at first ECG was 21.6 (15.6) years. All 12-lead ECGs from 967 patients with LQTS and 1092 who were evaluated for LQTS but discharged without this diagnosis were included for AI-ECG analysis. Based on the ECG-derived QTc alone, patients were classified with an area under the curve (AUC) value of 0.824 (95% CI, 0.79-0.858); using AI-ECG, the AUC was 0.900 (95% CI, 0.876-0.925). Furthermore, in the subset of patients who had a normal resting QTc (<450 milliseconds), the QTc alone distinguished those with LQTS from those without LQTS with an AUC of 0.741 (95% CI, 0.689-0.794), whereas the AI-ECG increased this discrimination to an AUC of 0.863 (95% CI, 0.824-0.903). In addition, the AI-ECG was able to distinguish the 3 main genotypic subgroups (LQT1, LQT2, and LQT3) with an AUC of 0.921 (95% CI, 0.890-0.951) for LQT1 compared with LQT2 and 3, 0.944 (95% CI, 0.918-0.970) for LQT2 compared with LQT1 and 3, and 0.863 (95% CI, 0.792-0.934) for LQT3 compared with LQT1 and 2.CONCLUSIONS AND RELEVANCE In this study, the AI-ECG was found to distinguish pati...

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“…org). 5 In the past years, additional tools to more reliably assess LQTS on the ECG have been developed, [7][8][9] including the use of artificial intelligence in establishing the diagnosis. 10…”

Section: Diagnosismentioning

confidence: 99%

“…Based on ECGs of a large number of genotyped LQTS patients and their family members not carrying the familial variant, we designed an online calculator with information on the likelihood that LQTS is present based on the calculated QTc (https://www.qtcalculator.org). 5 In the past years, additional tools to more reliably assess LQTS on the ECG have been developed,7–9 including the use of artificial intelligence in establishing the diagnosis 10…”

Section: Introductionmentioning

confidence: 99%

Diagnosis, management and therapeutic strategies for congenital long QT syndrome

Wilde

Amin

Postema

2021

Heart

Self Cite

112

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Congenital long QT syndrome (LQTS) is characterised by heart rate corrected QT interval prolongation and life-threatening arrhythmias, leading to syncope and sudden death. Variations in genes encoding for cardiac ion channels, accessory ion channel subunits or proteins modulating the function of the ion channel have been identified as disease-causing mutations in up to 75% of all LQTS cases. Based on the underlying genetic defect, LQTS has been subdivided into different subtypes. Growing insights into the genetic background and pathophysiology of LQTS has led to the identification of genotype–phenotype relationships for the most common genetic subtypes, the recognition of genetic and non-genetic modifiers of phenotype, optimisation of risk stratification algorithms and the discovery of gene-specific therapies in LQTS. Nevertheless, despite these great advancements in the LQTS field, large gaps in knowledge still exist. For example, up to 25% of LQTS cases still remain genotype elusive, which hampers proper identification of family members at risk, and it is still largely unknown what determines the large variability in disease severity, where even within one family an identical mutation causes malignant arrhythmias in some carriers, while in other carriers, the disease is clinically silent. In this review, we summarise the current evidence available on the diagnosis, clinical management and therapeutic strategies in LQTS. We also discuss new scientific developments and areas of research, which are expected to increase our understanding of the complex genetic architecture in genotype-negative patients, lead to improved risk stratification in asymptomatic mutation carriers and more targeted (gene-specific and even mutation-specific) therapies.

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Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis

Cited by 23 publications

References 21 publications

Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models

Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models

Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram

Diagnosis, management and therapeutic strategies for congenital long QT syndrome

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