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 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 …

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 …

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 Convolutional neural networks (CNNs) are a subtype of artificial neural network
that have shown strong performance in computer vision tasks including image classification …

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

Objective Data sets with demographic imbalances can introduce bias in deep learning
models and potentially amplify existing health disparities. We evaluated the reporting of …

Importance Chest radiography is the most common diagnostic imaging test in medicine and
may also provide information about longevity and prognosis. Objective To develop and test …

Background The performance of a deep learning (DL) algorithm should be validated in
actual clinical situations, before its clinical implementation. Purpose To evaluate the …

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 …