ArticleJournal of clinical medicine2021
Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Sex estimation from hand-wrist radiographic images: an artificial intelligence approach.International journal of legal medicine · 2026Article
- Explainable AI for chest radiographs: sex-stratified fairness auditing in CNN-based pneumonia detection.Scientific reports · 2026Article
- Multimodal model integrating ultrasound and demographic data for the diagnosis of knee osteoarthritis.BMC medical imaging · 2026Article
- Will AI Replace Physicians in the Near Future? AI Adoption Barriers in Medicine.Diagnostics (Basel, Switzerland) · 2026Review
- Predicting biological sex in pediatric skeleton X-rays using artificial intelligence.Scientific reports · 2025Article
- 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
- Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning.Korean journal of radiology · 2023Review
- An Artificial Neural Network for Nasogastric Tube Position Decision Support.Radiology. Artificial intelligence · 2023Article
- Pediatric age estimation from thoracic and abdominal CT scout views using deep learning.Scientific reports · 2023Article
- Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis.Communications medicine · 2022Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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 adults based on chest radiographs (CXRs).
methodsIn this retrospective study, 66,643 CXRs of 47,060 healthy adults were used for model training and testing. In total, 47,060 individuals (mean age ± standard deviation, 38.7 ± 11.9 years; 22,144 males) were included. By using chronological ages as references, mean absolute error (MAE), root mean square error (RMSE), and Pearson's correlation coefficient were used to assess the model performance. Summarized class activation maps were used to highlight the activated anatomical regions. The area under the curve (AUC) was used to examine the validity for sex prediction.
resultsWhen model predictions were compared with the chronological ages, the MAE was 2.1 years, RMSE was 2.8 years, and Pearson's correlation coefficient was 0.97 (
conclusionDeep learning can accurately estimate age and sex based on CXRs.
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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.