The researches find that the electrocardiogram (ECG) investigators and clinicians have sought to expand the diagnostic potential of this convenient, widely available and relatively inexpensive noninvasive examination. Therefore In this issue of the Journal of Cardiovascular Electrophysiology; Attia and colleagues demonstrate the ability of a novel algorithm based on the standard 12-lead ECG developed with deep learning artificial intelligence (AI) to detect patients with reduced left ventricular ejection fraction (rLVEF).
The electrocardiogram (ECG)
This form of AI, also call machine learning allows computer programs to identify relationships directly from data. This appear to be well-suited for ECGs; Because which consist of digitize data based on time and voltage from various sites on the body surface. Therefore Using AI to identify patients with rLVEF might be useful in patients for whom echocardiography or other imaging modalities are not indicate; not available or not cost-effective.
Early detection of rLVEF with the implementation of therapy may inhibit progression of the underlying disorder; prevent the development of symptoms; or reduce mortality. But This is support by a study involving screening base on elevate brain-type natriuretic peptide (BNP) follow by echocardiography and appropriate intervention; which reduced the combine rates of rLVEF; diastolic dysfunction, and heart failure (HF).
The implementation of therapy
ECG changes reflecting rLVEF are biologically plausible because disorders that result in rLVEF may alter cardiac electrical impulse creation; propagation or repolarization in atrial; But ventricular, and conduction tissue by multiple mechanisms such as direct injury, remodeling, and alterations in neuroendocrine activity. If these disturbances are of sufficient magnitude; they may be detectable by the ECG.
On the other hand detection of signals due to rLVEF is not straightforward as there are many electrophysiological disorders that result in major changes in the ECG without rLVEF that could mimic or distort the signals related to rLVEF; such as genetic or acquired conduction and repolarization disturbances, atrial or ventricular arrhythmias, and paced rhythms. Furthermore, surface manifestations of rLVEF was obscure by external electromagnetic signals, skeletal muscle electrical signals; ECG filters, poor electrode contact, incorrect electrode placement; or unusual body habitus.
Attia et al report impressive statistics on the ability of the AI-ECG model (AI-ECG) to detect patients with LVEF ≤35%. Three population samples are reported. Therefore The first sample was comprised of 6008 adult patients who underwent a standard 12-lead ECG at the Mayo Clinic ECG laboratory between September 1 and 30; 2018 and who had a clinically indicated comprehensive echocardiogram within 12 months of the ECG.
The AI-ECG detected patients with LVEF ≤35% with accuracy, specificity; and sensitivity of 86.0%, 86.3%, and 81.5%, respectively. The area under the receiver operating characteristic (ROC) curve (c-statistic) was 0.911, which suggests excellent discrimination. The second sample was the subset of patients who had the echocardiogram performed within a month of the ECG (n = 3874).