ArticleScientific reports2025
Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparison of dual-energy X-ray absorptiometry and lumbar radiographs using deep learning for osteoporosis diagnosis.Journal of bone and mineral metabolism · 2026Article
- Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Deep learning in detection of osteoporosis: insights from recent evidence, and future directions.Annals of medicine and surgery (2012) · 2026Article
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19 authors.
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Abstract
Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a significantly increased risk of fragility fractures. To prevent such fractures in patients with cervical disease, treating osteoporosis is an important strategy. This study aimed to validate the diagnostic yield of a deep learning algorithm for detecting osteopenia/osteoporosis using cervical radiography and compare its diagnostic accuracy with that of spine surgeons. Samples were divided into training (n = 200) and test (n = 30) datasets. The deep learning algorithm, designed to detect T-scores of the femoral neck or lumbar spine <-1.0 using cervical radiography, was constructed using a convolutional neural network model. The number of correct diagnoses was compared between the algorithm and nine spine surgeons using the independent test dataset. The results indicated that the algorithm's diagnostic accuracy, sensitivity, and specificity in the independent test dataset were 0.800, 0.818, and 0.750, respectively. The rate of corrected answers by the deep learning algorithm was significantly higher than that of nine spine surgeons in the test dataset (80.0% vs. 60.6%; p = 0.032). In conclusion, the diagnostic yield of the algorithm was higher than that of spine surgeons.
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