ArticleEmergency radiology2020
Estimation of age in unidentified patients via chest radiography using convolutional neural network regression.
Article in Emergency radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
- Predicting pediatric age from chest X-rays using deep learning: a novel approach.Insights into imaging · 2025Article
- Prediction of a Cephalometric Parameter and Skeletal Patterns from Lateral Profile Photographs: A Retrospective Comparative Analysis of Regression Convolutional Neural Networks.Journal of clinical medicine · 2024Article
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
- 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
- Prediction of body weight from chest radiographs using deep learning with a convolutional neural network.Radiological physics and technology · 2023Article
- A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis.Scientific reports · 2023Article
- Deep learning-based age estimation from clinical Computed Tomography image data of the thorax and abdomen in the adult population.PloS one · 2023Article
- Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis.Communications medicine · 2022Article
- Quantitative estimation of pulmonary artery wedge pressure from chest radiographs by a regression convolutional neural network.Heart and vessels · 2022Article
- Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.Scientific reports · 2022Article
- Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data.Sensors (Basel, Switzerland) · 2022Article
- Development and evaluation of a double-check support system using artificial intelligence in endoscopic screening for gastric cancer.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2022Article
- Radiology "forensics": determination of age and sex from chest radiographs using deep learning.Emergency radiology · 2021Article
- Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs.Journal of clinical medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePatient age has important clinical utility for refining a differential diagnosis in radiology. Here, we evaluate the potential for convolutional neural network models to predict patient age based on anterior-posterior chest radiographs for instances where patients may present for emergency services without the ability to provide this identifying information.
methodsWe used the CheXpert dataset of 224,316 chest radiographs from 65,240 patients to train CNN regression models with ResNet50 and DenseNet121 architectures for prediction of patient age based on anterior-posterior (AP) view chest radiographs. We evaluate these models on both the CheXpert validation dataset and a local hospital case in which a patient initially presented for emergency services intubated and without identification.
resultsMean absolute error (MAE) for our ResNet50 model on the CheXpert dataset is 4.94 years for predicting patient age based on AP chest radiographs. MAE for our DenseNet121 model is 4.69 years. Both models have a correlation coefficient between true patient ages and predicted ages of 0.944. Wilcoxon rank-sum comparison between the two model architectures shows no significant difference (p = 0.33), but both show improvement over a baseline demographic-driven estimation (p < 0.001).
conclusionsFor circumstances in which patients present for healthcare services without readily accessible identification such as in the setting trauma or altered mental status, CNN regression models for age prediction have potential clinical utility for refining estimates related to this missing patient information.
Indexed as
Identifiers
What OpenQuestion holds
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.