ArticleRadiology. Artificial intelligence2022
Deep Radiomics-based Approach to the Diagnosis of Osteoporosis Using Hip Radiographs.
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Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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14citing papers in PubMed, 2 pooled it
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Diagnostic performance of radiomics for predicting osteoporosis in adults: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Pooled it
- A practical 10-step recipe for conducting radiomic studies.European radiology experimental · 2026Review
- The Impact of Radiomics Image Analysis on Adult Hip Pathologies: A Scoping Review.Journal of clinical medicine · 2026Review
- An AI-based pipeline for osteoporosis/osteopenia prediction using hip radiographs.Skeletal radiology · 2026Article
- Current Status of AI-Assisted Screening for Opportunistic Osteoporosis.Current osteoporosis reports · 2026Review
- Stability-Driven Osteoporosis Screening: Multi-View Consensus Feature Selection with External Validation and Sensitivity Analysis.Journal of clinical medicine · 2026Article
- Comprehensive opportunistic osteoporosis screening with AI: retrospective development and prospective validation of dual-site BMD prediction from a single radiograph.Frontiers in endocrinology · 2026Article
- Multi-Spectral MRI-Based Radiomic Features Can Classify Femoral Loosening in Patients With Total Hip Arthroplasty.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Article
- Radiomics in FAI: current status and perspectives.Bone & joint research · 2025Article
- A comprehensive predictive model for postoperative joint function in robot-assisted total hip arthroplasty patients: combining radiomics and clinical indicators.Journal of robotic surgery · 2024Article
- Deep learning for osteoporosis screening using an anteroposterior hip radiograph image.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2024Article
- Clinical Validation of a Deep Learning-Based Software for Lumbar Bone Mineral Density and T-Score Prediction from Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- Automated osteoporosis classification andTherapeutic advances in musculoskeletal disease · 2024Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Sangwook Kim *Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).ORCID https://orcid.org/0000-0001-6482-9561
Bo Ram Kim *Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).ORCID 0000-0002-5968-3884
Hee-Dong ChaeDepartment of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).ORCID 0000-0003-2624-1606
Jimin LeeDepartment of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).ORCID 0000-0002-3049-5687
Sung-Joon YeDepartment of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).ORCID 0000-0001-8714-6317
Dong Hyun KimDepartment of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).ORCID 0000-0002-3871-7002
Sung Hwan HongDepartment of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).ORCID 0000-0003-2302-1341
Ja-Young ChoiDepartment of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).ORCID 0000-0002-3363-0629
Hye Jin YooDepartment of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).ORCID 0000-0002-9704-7870
Seoul National University · KRSeoul National University Hospital · KRSeoul National University of Science and Technology · KR
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop and validate deep radiomics models for the diagnosis of osteoporosis using hip radiographs. Materials and Methods: A deep radiomics model was developed using 4924 hip radiographs from 4308 patients (3632 women; mean age, 62 years ± 13 [SD]) obtained between September 2009 and April 2020. Ten deep features, 16 texture features, and three clinical features were used to train the model. T score measured with dual-energy x-ray absorptiometry was used as a reference standard for osteoporosis. Seven deep radiomics models that combined different types of features were developed: clinical (model C); texture (model T); deep (model D); texture and clinical (model TC); deep and clinical (model DC); deep and texture (model DT); and deep, texture, and clinical features (model DTC). A total of 444 hip radiographs obtained between January 2019 and April 2020 from another institution were used for the external test. Six radiologists performed an observer performance test. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic performance. Results: For the external test set, model D (AUC, 0.92; 95% CI: 0.89, 0.95) demonstrated higher diagnostic performance than model T (AUC, 0.77; 95% CI: 0.70, 0.83; adjusted Conclusion: Deep radiomics models using hip radiographs could be used to diagnose osteoporosis with high performance.
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Absorptiometry/Bone DensitometryHipSkeletal-Appendicular
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