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 …

Objectives Chest X-rays (CXRs) convey much illegible physiological information that deep
learning model (DLM) has been reported interpreting successfully. Since the …

Purpose To develop and test the performance of deep convolutional neural networks
(DCNNs) for automated classification of age and sex on chest radiographs (CXR). Methods …

Background Deep learning models are increasingly informing medical decision making, for
instance, in the detection of acute intracranial hemorrhage and pulmonary embolism …

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 …

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 …

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 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 …

Chest X-ray (CXR) is one of the most commonly performed medical imaging tests. Although
aging, sex and disease status have been known to cause changes in CXR findings, the …

Background Deep convolutional neural networks (DCNNs) for diagnosis of disease on chest
radiographs (CXR) have been shown to be biased against males or females if the datasets …