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There is no established screening approach for hypertrophic cardiomyopathy (HCM). We recently developed an artificial intelligence (AI) model for the detection of HCM based on the 12‑lead electrocardiogram (AI-ECG) in adults. Here, we aimed to validate this approach of ECG-based HCM detection in pediatric patients (age ≤ 18 years).We identified a cohort of 300 children and adolescents with HCM (mean age 12.5 ± 4.6 years, male 68%) who had an ECG and echocardiogram at our institution. Patients were age- and sex-matched to 18,439 non-HCM controls. Diagnostic performance of the AI-ECG model for the detection of HCM was estimated using the previously identified optimal diagnostic threshold of 11% (the probability output derived by the model above which an ECG is considered to belong to an HCM patient).Mean AI-ECG probabilities of HCM were 92% and 5% in the case and control groups, respectively. The area under the receiver operating characteristic curve (AUC) of the AI-ECG model for HCM detection was 0.98 (95% CI 0.98-0.99) with corresponding sensitivity 92% and specificity 95%. The positive and negative predictive values were 22% and 99%, respectively. The model performed similarly in males and females and in genotype-positive and genotype-negative HCM patients. Performance tended to be superior with increasing age. In the age subgroup5 years, the test's AUC was 0.93. In comparison, the AUC was 0.99 in the age subgroup 15-18 years.A deep-learning, AI model can detect pediatric HCM with high accuracy from the standard 12‑lead ECG.

Background An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients. Method We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG and TTE within 7 days, at least one of which was during hospitalization. Discrimination of the AI-ECG for LVSD was determined using receiver-operator characteristic curve (AUC) values. Results We included 5680 patients with a mean age of 68 ± 15 years (37% females). Acute coronary syndrome (ACS) was present in 55%. LVSD was present in 34% of patients (mean LVEF 48 ± 16%). The AI-ECG had an AUC of 0.83 (95% confidence interval 0.82–0.84) for discrimination of LVSD. Using the optimal cut-off, the AI-ECG had 73%, specificity 78%, negative predictive value 85% and overall accuracy 76% for LVSD. AUC values were higher for patients aged Conclusions The AI-ECG algorithm had very good discrimination for LVSD in this critically-ill CICU cohort with a high prevalence of LVSD. Performance was better in younger male patients and those without ACS, highlighting those CICU patients in whom screening for LVSD using AI ECG may be more effective. The AI-ECG might potentially be useful for identification of LVSD in resource-limited settings when TTE is unavailable.

The presence of left ventricular systolic dysfunction (LVSD) alters clinical management and prognosis in most acute and chronic cardiovascular conditions. While transthoracic echocardiography (TTE) remains the most common diagnostic tool to screen for LVSD, it is operator-dependent, time-consuming, effort-intensive, and relatively expensive. Recent work has demonstrated the ability of an artificial intelligence-augment ECG (AI-ECG) model to accurately predict LVSD in critical intensive care unit (CICU) patients. We demonstrate that the AI-ECG algorithm can maintain its performance in these patients with and without AF despite their clinical differences. An AI-ECG algorithm can serve as a non-invasive, inexpensive, and rapid screening tool for early detection of LVSD in resource-limited settings, and potentially expedite clinical decision making and guideline-directed therapies in the acute care setting.

Coronary microvascular dysfunction (CMD) is linked to adverse cardiovascular events. Definitive diagnosis of CMD requires invasive provocative testing during angiography. We developed and tested a novel computerized T wave analysis tool to identify electrocardiographic signatures of CMD.1552 patients underwent an invasive assessment of coronary microvascular function. Patients with interpretable pre-procedural ECGs were divided into 2 age and sex matched groups (n=261 in each group, 75% female): normal microvascular function, CFR2.5 (CFR+), and abnormal microvascular function, CFR ≤ 2.5 (CFR-). ECGs were evaluated using a novel T wave program that quantified subtle changes in T wave morphology.T wave repolarization parameters were significantly different between patients with normal and abnormal microvascular function. The top 3 features in males comprised of T wave area in V6 (CFR+: 10091.4 mV(2) vs. CFR-: 8152.3 mV(2), p0.05); T1 Y-center of gravity in lead II (CFR+: 17.8 mV vs. CFR-: 22.4, p0.005) and T Peak-T End in lead II (CFR+: 97.6 msec vs. CFR-: 91.1 msec, p0.05). These could identify the presence of an abnormal CFR with 74 ± 0.2% accuracy. In females, the top 3 features were T wave right slope lead V6 (CFR+: -2489.1 mV/msec vs. CFR-: -2352.3 mV/msec, p0.005); Amplitude in V6 (CFR+: 190.4 mV vs. 172.7 mV, p=0.05) and Y-center of gravity in lead V1 (CFR+: 33.3 vs. CFR-: 40.0, p=0.001). These features could identify the presence of an abnormal CFR with 67 ± 0.3% accuracy.Our data demonstrates that a computer-based repolarization measurement tool may identify electrocardiographic signatures of CMD.