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 …

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 …

Aging is an important risk factor for disease, leading to morphological change that can be
assessed on Computed Tomography (CT) scans. We propose a deep learning model for …

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 …

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 …

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 Adult age estimation (AAE) is a challenging task. Deep learning (DL) could be a
supportive tool. This study aimed to develop DL models for AAE based on CT images and …

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 …

Background Chest radiographs are widely available and cost-effective; however, their
usefulness as a biomarker of ageing using multi-institutional data remains underexplored …

Age assessment is regularly used in clinical routine by pediatric endocrinologists to
determine the physical development or maturity of children and adolescents. Our study …