ArticleEmergency radiology2021
Radiology "forensics": determination of age and sex from chest radiographs using deep learning.
Article in Emergency radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.Pediatric radiology · 2022Pooled it
- AI alignment in medical imaging: Unveiling hidden biases through counterfactual analysis.PLOS digital health · 2026Article
- AI-driven differentiation of 2D proximal femur morphometry and hounsfield units for integrated forensic estimation of sex, stature, and age in a modern Thai dry bone sample.Anatomical science international · 2026Article
- Age estimation through CT image analysis of the thoracic cage in a Mediterranean population.International journal of legal medicine · 2026Article
- Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses.NPJ digital medicine · 2025Review
- Automated Analysis of Vertebral Body Surface Roughness for Adult Age Estimation: Ellipse Fitting and Machine-Learning Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Learning and diSentangling patient static information from time-series Electronic hEalth Records (STEER).PLOS digital health · 2024Article
- Acquisition parameters influence AI recognition of race in chest x-rays and mitigating these factors reduces underdiagnosis bias.Nature communications · 2024Article
- Demographic bias in misdiagnosis by computational pathology models.Nature medicine · 2024Article
- 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
- Risk of Bias in Chest Radiography Deep Learning Foundation Models.Radiology. Artificial intelligence · 2023Article
- The path toward equal performance in medical machine learning.Patterns (New York, N.Y.) · 2023Review
- Algorithmic encoding of protected characteristics in chest X-ray disease detection models.EBioMedicine · 2023Article
- Confounders mediate AI prediction of demographics in medical imaging.NPJ digital medicine · 2022Article
- Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis.Communications medicine · 2022Article
- AI recognition of patient race in medical imaging: a modelling study.The Lancet. Digital health · 2022Article
- Deep learning prediction of sex on chest radiographs: a potential contributor to biased algorithms.Emergency radiology · 2022Article
- Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR).
methodsWe obtained 112,120 frontal CXRs from the NIH ChestX-ray14 database performed in 48,780 females (44%) and 63,340 males (56%) ranging from 1 to 95 years old. The dataset was split into training (70%), validation (10%), and test (20%) datasets, and used to fine-tune ResNet-18 DCNNs pretrained on ImageNet for (1) determination of sex (using entire dataset and only pediatric CXRs); (2) determination of age < 18 years old or ≥ 18 years old (using entire dataset); and (3) determination of age < 11 years old or 11-18 years old (using only pediatric CXRs). External testing was performed on 662 CXRs from China. Area under the receiver operating characteristic curve (AUC) was used to evaluate DCNN test performance.
resultsDCNNs trained to determine sex on the entire dataset and pediatric CXRs only had AUCs of 1.0 and 0.91, respectively (p < 0.0001). DCNNs trained to determine age < or ≥ 18 years old and < 11 vs. 11-18 years old had AUCs of 0.99 and 0.96 (p < 0.0001), respectively. External testing showed AUC of 0.98 for sex (p = 0.01) and 0.91 for determining age < or ≥ 18 years old (p < 0.001).
conclusionDCNNs can accurately predict sex from CXRs and distinguish between adult and pediatric patients in both American and Chinese populations. The ability to glean demographic information from CXRs may aid forensic investigations, as well as help identify novel anatomic landmarks for sex and age.
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