ArticleCommunications medicine2022
Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis.
Article in Communications medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Deep learning-derived retinal age gap and its associations with lifestyle, systemic, and ocular health in a health screening cohort.GeroScience · 2026Article
- Bone age estimation from chest radiographs using deep neural networks: a proof-of-concept study.Pediatric radiology · 2026Article
- Functional, molecular, and digital measurements of biological age.The Journal of clinical investigation · 2026Review
- Imaging-derived biological age across multiple organs links to mortality and aging-related health outcomes.npj aging · 2026Article
- A generative model uses healthy and diseased image pairs for pixel-level chest X-ray pathology localization.Nature biomedical engineering · 2026Article
- Will AI Replace Physicians in the Near Future? AI Adoption Barriers in Medicine.Diagnostics (Basel, Switzerland) · 2026Review
- Development of an age estimation method for the coxal bone and lumbar vertebrae obtained from post-mortem computed tomography images using a convolutional neural network.International journal of legal medicine · 2026Article
- Mammo-AGE: deep learning estimation of breast age from mammograms.Nature communications · 2025Article
- A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning.NPJ digital medicine · 2025Article
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- External Testing of a Deep Learning Model to Estimate Biologic Age Using Chest Radiographs.Radiology. Artificial intelligence · 2024Article
- Chest Radiographs as Biological Clocks: Implications for Risk Stratification and Personalized Care.Radiology. Artificial intelligence · 2024Article
- Deep learning to predict long-term mortality from plain chest X-ray in patients referred for suspected coronary artery disease.Journal of thoracic disease · 2024Article
- Patient Re-Identification Based on Deep Metric Learning in Trunk Computed Tomography Images Acquired from Devices from Different Vendors.Journal of imaging informatics in medicine · 2024Article
- Deep learning-based age estimation from chest CT scans.International journal of computer assisted radiology and surgery · 2024Article
- Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications.Radiology. Artificial intelligence · 2023Article
- Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography.Journal of imaging · 2023Review
- Identification of age-dependent features of human bronchi using explainable artificial intelligence.ERJ open research · 2023Article
Corrections and comments
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Authors and funding
27 authors.
Funding
Abstract
backgroundIn recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined.
methodsTo address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease.
resultsThe DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease.
conclusionsOur results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.