Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs

Figure 1.. Construction of neural network architecture. Shown is the construction of the neural network architecture for the age and sex networks. Further details are discussed under the methods section.

Figure 2.. Receiver operating characteristic (ROC) of sex classification. A, Shown is the ROC curve for sex classification in the validation set. Overall area under the curve (AUC) was 0.97. B, Shows a separate network derived from specific ECG features—QRS duration, area of the T wave under V4, the time to peak of the P wave in lead I, the QTc interval, and the area under the P wave in lead I.

Figure 3.. Convolutional neural network (CNN)-predicted age vs reported age. A, Shown is the estimated CNN-predicted ECG age (blue) vs the reported chronologic age (in years; red—identity line). The model R squared was 0.7 with a Pearson correlation of r=0.837. B, Demonstrates a multi-group classification to the age (in years) groups of 18 to 25, 25 to 50, 50 to 75, and 75 and accuracy of CNN-predicted age (x axis—estimated age) vs the actual age (y axis) in terms of the percentage of patients with a specific actual age who had a specific corresponding CNN-predicted age within a similar range (eg, a patient from 18–25 y of age having a CNN-predicted age from 18–25 y).

Figure 4.. R-squared distribution for correlation between convolutional neural network (CNN)-predicted age and chronologic age. Shown is the distribution of the number of patients with specific R-squared values correlating CNN-predicted age (listed as ECG age in x axis) with chronologic age. Higher R squared values suggest a closer linear correlation between ages while low R squared values suggest poor correlation over time.

Figure 5.. Correlation between convolutional neural network (CNN) predicted age and chronologic age in different patients. A, Shown is the correlation of CNN-predicted age (in years) with chronologic age (in years) at every year of life in an otherwise healthy patient. Note the linear association of age. Also note that the CNN-predicted age is younger than the chronological age. B, Shows correlation between CNN-predicted age and chronologic age in a woman with pulmonary hypertension with incident major hospitalizations. She was chronically managed with beta-blockers but developed supraventricular tachycardia and acute pulmonary embolus at age 22. In the second year, she developed acute cardiogenic and hemodynamic shock due to endocarditis and acute right ventricular failure. Thereafter, she was well controlled on medications for her pulmonary hypertension without further major incident events. With stabilization of her medical condition her CNN-predicted age dropped and matched her chronological age after age 23. C, shows an example of a patient with multiple incident myocardial infarctions, low ejection fraction, and eventual cardiac transplant. Before age 54, he had numerous myocardial…

Figure 6.. Example electrocardiograms from patients comparing actual age and sex against CNN-predicted age and sex. Shown are 4 examples of patient ECGs. Patient (A) is a moderately healthy man without significant cardiovascular disease whose CNN-predicted age and sex were similar to actual. Similarly, patient (C) is a moderately healthy woman without significant cardiovascular disease whose CNN-predicted age and sex were similar to actual. Patient (B) is a man with history of myocardial infarction, hypertension, and diabetes mellitus whose CNN-predicted age was >10 y above his actual age. Finally, patient (D) is a very healthy 85 y old woman with no significant diseases who underwent a routine stress test as part of a general check-up.

Figure 7.. Potential use of linear regression modeling of ECG-estimated age progression as a biomarker. Shown are examples from 2 patients—one whose ECG-estimated age increased faster than chronologic age (A) and one whose ECG-estimated age increased slower than chronologic age (B). In these 2 patients’ cases, patient (A) had an incident MI around age 50 and eventually developed a low ejection fraction. Patient (B) had hospitalizations in his late 60s for infectious and cancer issues but had no significant cardiovascular disease later in life along with resolution of his infectious and cancer diagnoses. The patient in (A) died at the age of 65 due to cardiovascular causes while the patient in (B) is still alive at the age of 85.