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

Abstract

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...

Diagnosis, management and therapeutic strategies for congenital long QT syndrome

“…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…”

“…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…”

Automated ECG Interpretation—A Brief History from High Expectations to Deepest Networks

“…This leads to the concept of certain packages based on AI being used in a very specific group of patients. A good example of this is the ability of an AI-based system to detect concealed long QT syndrome, where conventionally this is diagnosed when the QT interval exceeds a fixed threshold such as 500 ms Now, an AI-based system can report long QT syndrome when the QT interval is less than 450 ms [68], with confirmation being achieved via appropriate genetic testing. However, this approach would appear to be suited to use in a clinic where individuals suspected of having long QT or being screened for familial long QT are involved, but if applied to the general population, might result in a very high percentage of false positive reports of concealed long QT.…”

Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome

“…17 Techniques such as deep-learning, including convolutional neural networks (CNN), are bringing a radical change in the field of pattern recognition, improving earlier models in learning tasks such as image classification, 5 ECG analysis and natural language processing. [18][19][20] Herein, we tested if such models were able to learn the ECG footprint of sotalol, an IKr-blocker drug inducing TdP, to develop a new tool using ECG to recognize beyond QTc, exposure to IKr-blocker drugs; improve prediction of drug-induced TdP events and classification of cLQTS types particularly cLQT2.…”