Background In recent years, there has been considerable research on the use of artificial
intelligence to estimate age and disease status from medical images. However, age …

Objectives The goal of this study was to assess whether a deep learning estimate of age
from a chest radiograph image (CXR-Age) can predict longevity beyond chronological age …

Purpose Medical imaging can be used to estimate a patient's biological age, which may
provide complementary information to clinicians compared to chronological age. In this …

Age prediction based on appearances of different anatomies in medical images has been
clinically explored for many decades. In this paper, we used deep learning to predict a …

Purpose Patient age has important clinical utility for refining a differential diagnosis in
radiology. Here, we evaluate the potential for convolutional neural network models to predict …

Objective The biological age progression of the heart varies from person to person. We
developed a deep learning model (DLM) to predict the biological age via ECG to explore its …

Background: The performance of chest radiography-based age and sex prediction has not
been well validated. We used a deep learning model to predict the age and sex of healthy …

The electrocardiogram (ECG) is the most commonly used exam for the evaluation of
cardiovascular diseases. Here we propose that the age predicted by artificial intelligence …

Determining the discrepancy between chronological and physiological age of patients is
central to preventative and personalized care. Electronic medical records (EMR) provide rich …

Background There is a paucity of data on artificial intelligence-estimated biological
electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular …