Artificial intelligence using electrocardiography: strengths and pitfalls

An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review

Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.

A Novel Quantitative Electrocardiography Strategy Reveals the Electroinhibitory Effect of Tamoxifen on the Mouse Heart

Tamoxifen, a selective estrogen receptor modulator, was initially used to treat cancer in women and more recently to induce conditional gene editing in rodent hearts. However, little is known about the baseline biological effects of tamoxifen on the myocardium. In order to clarify the short-term effects of tamoxifen on cardiac electrophysiology of myocardium, we applied a single-chest-lead quantitative method and analyzed the short-term electrocardiographic phenotypes induced by tamoxifen in the heart of adult female mice. We found that tamoxifen prolonged the PP interval and caused a decreased heartbeat, and further induced atrioventricular block by gradually prolonging the PR interval. Further correlation analysis suggested that tamoxifen had a synergistic and dose-independent inhibition on the time course of the PP interval and PR interval. This prolongation of the critical time course may represent a tamoxifen-specific ECG excitatory-inhibitory mechanism, leading to a reduction in the number of supraventricular action potentials and thus bradycardia. Segmental reconstructions showed that tamoxifen induced a decrease in the conduction velocity of action potentials throughout the atria and parts of the ventricles, resulting in a flattening of the P wave and R wave. In addition, we detected the previously reported prolongation of the QT interval, which may be due to a prolonged duration of the ventricular repolarizing T wave rather than the depolarizing QRS complex. Our study highlights that tamoxifen can produce patterning alternations in the cardiac conduction system, including the formation of inhibitory electrical signals with reduced conduction velocity, implying its involvement in the regulation of myocardial ion transport and the mediation of arrhythmias. A Novel Quantitative Electrocardiography Strategy Reveals the Electroinhibitory Effect of Tamoxifen on the Mouse Heart（Figure 9）. A working model of tamoxifen producing acute electrical disturbances in the myocardium. SN, sinus node; AVN, atrioventricular node; RA, right atrium; LA, left atrium; RV, right ventricle; LV, left ventricle.

Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG

BACKGROUND

We developed and validated an artificial intelligence (AI)-enabled smartwatch ECG to detect heart failure-reduced ejection fraction (HFrEF).

METHODS

This was a cohort study involving two hospitals (A and B). We developed the AI in two steps. First, we developed an AI model (ECGT2T) to synthesize ten-lead ECG from the asynchronized 2-lead ECG (Lead I and II). ECGT2T is a deep learning model based on a generative adversarial network, which translates source ECGs to reference ECGs by learning styles of the reference ECGs. For this, we included adult patients aged ≥18 years from hospital A with at least one digitally stored 12-lead ECG. Second, we developed an AI model to detect HFrEF using a 10 s 12-lead ECG. The AI model was based on convolutional neural network. For this, we included adult patients who underwent ECG and echocardiography within 14 days. To validate the AI, we included adult patients from hospital B who underwent two-lead smartwatch ECG and echocardiography on the same day. The AI model generates a 10 s 12-lead ECG from a two-lead smartwatch ECG using ECGT2T and detects HFrEF using the generated 12-lead ECG.

RESULTS

We included 137,673 patients with 458,745 ECGs and 38,643 patients with 88,900 ECGs from hospital A for developing the ECGT2T and HFrEF detection models, respectively. The area under the receiver operating characteristic curve of AI for detecting HFrEF using smartwatch ECG was 0.934 (95% confidence interval 0.913-0.955) with 755 patients from hospital B. The sensitivity, specificity, positive predictive value, and negative predictive value of AI were 0.897, 0.860, 0.258, and 0.994, respectively.

CONCLUSIONS

An AI-enabled smartwatch 2-lead ECG could detect HFrEF with reasonable performance.