ArticleScientific reports2022
DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 66 citations in OpenAlex.
- Ethical and Legal Concerns of Deepfake Technology in Biomedical Imaging: A Comprehensive Survey.Annals of biomedical engineering · 2026Review
- Moderate-to-substantial agreement of ChatGPT-5 for Kellgren-Lawrence grading on synthetic knee radiographs: a controlled cross-sectional observer agreement study.Rheumatology international · 2026Article
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Comparative evaluation of generative artificial intelligence models for synthetic knee radiograph augmentation in clinical research.BMC medical imaging · 2026Article
- AI-generated medical realism and hantavirus misinformation: a new public health threat?Frontiers in digital health · 2026Article
- Detectability and healthcare implications of generative AI-synthesized chest radiographs: a blinded radiologist reader study.Frontiers in medicine · 2026Article
- Next-generation osteoarthritis models: integrating biological, computational, and engineering approaches.Stem cell research & therapy · 2025Review
- Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models.NPJ digital medicine · 2025Article
- Guided synthesis of annotated lung CT images with pathologies using a multi-conditioned denoising diffusion probabilistic model (mDDPM).Physics in medicine and biology · 2025Article
- Patient-Specific Variability in Interleukin-6 and Myeloperoxidase Responses in Osteoarthritis: Insights from Synthetic Data and Clustering Analysis.Journal of personalized medicine · 2025Article
- Generative AI in healthcare: challenges to patient agency and ethical implications.Frontiers in digital health · 2025Article
- The Surge of Artificial Intelligence (AI) in Scientific Writing: Who Will Hold the Rudder, You or AI?Hip & pelvis · 2024Article
- Generative AI in orthopedics: an explainable deep few-shot image augmentation pipeline for plain knee radiographs and Kellgren-Lawrence grading.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Possible Health Benefits and Risks of DeepFake Videos: A Qualitative Study in Nursing Students.Nursing reports (Pavia, Italy) · 2024Article
- Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning.Heliyon · 2024Article
- Generating Synthetic Data for Medical Imaging.Radiology · 2024Review
- Deep generative model of the distal tibial classic metaphyseal lesion in infants: assessment of synthetic images.Radiology advances · 2024Article
- Machine learning predicts upper secondary education dropout as early as the end of primary school.Scientific reports · 2024Article
- Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach.Journal of digital imaging · 2023Article
- Unsupervised domain adaptation for automated knee osteoarthritis phenotype classification.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Recent developments in deep learning have impacted medical science. However, new privacy issues and regulatory frameworks have hindered medical data sharing and collection. Deep learning is a very data-intensive process for which such regulatory limitations limit the potential for new breakthroughs and collaborations. However, generating medically accurate synthetic data can alleviate privacy issues and potentially augment deep learning pipelines. This study presents generative adversarial neural networks capable of generating realistic images of knee joint X-rays with varying osteoarthritis severity. We offer 320,000 synthetic (DeepFake) X-ray images from training with 5,556 real images. We validated our models regarding medical accuracy with 15 medical experts and for augmentation effects with an osteoarthritis severity classification task. We devised a survey of 30 real and 30 DeepFake images for medical experts. The result showed that on average, more DeepFakes were mistaken for real than the reverse. The result signified sufficient DeepFake realism for deceiving the medical experts. Finally, our DeepFakes improved classification accuracy in an osteoarthritis severity classification task with scarce real data and transfer learning. In addition, in the same classification task, we replaced all real training data with DeepFakes and suffered only a [Formula: see text] loss from baseline accuracy in classifying real osteoarthritis X-rays.
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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.