1. Tran O, Silverman S, Xu X, Bonafede M, Fox K, McDermott M, et al. Long-term direct and indirect economic burden associated with osteoporotic fracture in US postmenopausal women. Osteoporos Int 2021;32:1195-205.
[CROSSREF] [PUBMED] [PMC] [PDF]
2. Williams SA, Daigle SG, Weiss R, Wang Y, Arora T, Curtis JR. Economic burden of osteoporosis-related fractures in the US Medicare population. Ann Pharmacother 2021;55:821-9.
[CROSSREF] [PUBMED] [PMC] [PDF]
4. Stone KL, Seeley DG, Lui LY, Cauley JA, Ensrud K, Browner WS, et al. BMD at multiple sites and risk of fracture of multiple types: long-term results from the Study of Osteoporotic Fractures. J Bone Miner Res 2003;18:1947-54.
[CROSSREF] [PUBMED]
5. Kanis JA, Harvey NC, Johansson H, Oden A, Leslie WD, McCloskey EV. FRAX update. J Clin Densitom 2017;20:360-7.
[CROSSREF] [PUBMED]
6. Dimai HP. Use of dual-energy X-ray absorptiometry (DXA) for diagnosis and fracture risk assessment: WHO-criteria, T- and Z-score, and reference databases. Bone 2017;104:39-43.
[CROSSREF] [PUBMED]
7. Aspray TJ. New horizons in fracture risk assessment. Age Ageing 2013;42:548-54.
[CROSSREF] [PUBMED]
8. Hoiberg MP, Rubin KH, Hermann AP, Brixen K, Abrahamsen B. Diagnostic devices for osteoporosis in the general population: a systematic review. Bone 2016;92:58-69.
[CROSSREF] [PUBMED]
9. Marshall D, Johnell O, Wedel H. Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 1996;312:1254-9.
[CROSSREF] [PUBMED] [PMC]
10. Siris ES, Chen YT, Abbott TA, Barrett-Connor E, Miller PD, Wehren LE, et al. Bone mineral density thresholds for pharmacological intervention to prevent fractures. Arch Intern Med 2004;164:1108-12.
[CROSSREF] [PUBMED]
11. Keel S, Wu J, Lee PY, Scheetz J, He M. Visualizing deep learning models for the detection of referable diabetic retinopathy and glaucoma. JAMA Ophthalmol 2019;137:288-92.
[CROSSREF] [PUBMED] [PMC]
12. Sung J, Park S, Lee SM, Bae W, Park B, Jung E, et al. Added value of deep learning-based detection system for multiple major findings on chest radiographs: a randomized crossover study. Radiology 2021;299:450-9.
[CROSSREF] [PUBMED]
13. Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 2018;286:887-96.
[CROSSREF] [PUBMED]
14. Yamashita R, Nishio M, Do R, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging 2018;9:611-29.
[CROSSREF] [PUBMED] [PMC] [PDF]
15. Derkatch S, Kirby C, Kimelman D, Jozani MJ, Davidson JM, Leslie WD. Identification of vertebral fractures by convolutional neural networks to predict nonvertebral and hip fractures: a registry-based cohort study of dual X-ray absorptiometry. Radiology 2019;293:405-11.
[CROSSREF] [PUBMED]
16. Bluthgen C, Becker AS, Vittoria de Martini I, Meier A, Martini K, Frauenfelder T. Detection and localization of distal radius fractures: deep learning system versus radiologists. Eur J Radiol 2020;126:108925.
[CROSSREF] [PUBMED]
17. Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop 2017;88:581-6.
[CROSSREF] [PUBMED] [PMC]
18. Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. Eur Radiol 2020;30:3549-57.
[CROSSREF] [PUBMED] [PDF]
19. Loffler MT, Jacob A, Scharr A, Sollmann N, Burian E, El Husseini M, et al. Automatic opportunistic osteoporosis screening in routine CT: improved prediction of patients with prevalent vertebral fractures compared to DXA. Eur Radiol 2021;31:6069-77.
[CROSSREF] [PUBMED] [PMC] [PDF]
20. Shin CS, Kim MJ, Shim SM, Kim JT, Yu SH, Koo BK, et al. The prevalence and risk factors of vertebral fractures in Korea. J Bone Miner Metab 2012;30:183-92.
[CROSSREF] [PUBMED] [PDF]
22. Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol 2018;18:24.
[CROSSREF] [PUBMED] [PMC] [PDF]
23. Iyer S, Sowmya A, Blair A, White C, Dawes L, Moses D. A novel approach to vertebral compression fracture detection using imitation learning and patch based convolutional neural network. In: Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI); 2020 Apr 3-7; Iowa City, IA. Piscataway, NJ: IEEE; 2020;pp 726-30.
[CROSSREF]
24. de Vries B, Hegeman JH, Nijmeijer W, Geerdink J, Seifert C, Groothuis-Oudshoorn C. Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis. Osteoporos Int 2021;32:437-49.
[CROSSREF] [PUBMED] [PDF]
25. Xiao X, Wu Q. The utility of genetic risk score to improve performance of FRAX for fracture prediction in US postmenopausal women. Calcif Tissue Int 2021;108:746-56.
[CROSSREF] [PUBMED] [PMC] [PDF]
26. El-Hajj Fuleihan G, Chakhtoura M, Cauley JA, Chamoun N. Worldwide fracture prediction. J Clin Densitom 2017;20:397-424.
[CROSSREF] [PUBMED]
27. Han X, Zhang Y, Shao Y. On comparing 2 correlated C indices with censored survival data. Stat Med 2017;36:4041-9.
[CROSSREF] [PUBMED] [PMC] [PDF]
28. Ghosh S, Raja’S A, Chaudhary V, Dhillon G. Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis. In: SPIE Medical Imaging 2011; Computer-Aided Diagnosis. 2011 Feb 15; Orlando, FL:
https://doi.org/10.1117/12.878055.
[CROSSREF]
29. Wang Y, Yao J, Burns JE, Summers R. Osteoporotic and neoplastic compression fracture classification on longitudinal CT. In: Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI); 2016 Apr 13-16; Prague, CZ. Piscataway, NJ: IEEE; 2016;pp 1181-4.
[CROSSREF]
30. Bar A, Wolf L, Amitai OB, Toledano E, Elnekave E. Compression fractures detection on CT. In: SPIE Medical Imaging 2017: Computer-Aided Diagnosis; 2017 Feb 13-16; Orlando, FL.
https://doi.org/10.1117/12.2249635.
[CROSSREF]
31. Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 2018;98:8-15.
[CROSSREF] [PUBMED]
32. Muehlematter UJ, Mannil M, Becker AS, Vokinger KN, Finkenstaedt T, Osterhoff G, et al. Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning. Eur Radiol 2019;29:2207-17.
[CROSSREF] [PUBMED] [PDF]
33. Tecle N, Teitel J, Morris MR, Sani N, Mitten D, Hammert WC. Convolutional neural network for second metacarpal radiographic osteoporosis screening. J Hand Surg Am 2020;45:175-81.
[CROSSREF] [PUBMED]
34. Yamamoto N, Sukegawa S, Kitamura A, Goto R, Noda T, Nakano K, et al. Deep learning for osteoporosis classification using hip radiographs and patient clinical covariates. Biomolecules 2020;10:1534.
[CROSSREF] [PUBMED] [PMC]
35. Su Y, Kwok T, Cummings SR, Yip B, Cawthon PM. Can classification and regression tree analysis help identify clinically meaningful risk groups for hip fracture prediction in older American men (the MrOS cohort study)? JBMR Plus 2019;3:e10207.
[CROSSREF] [PUBMED] [PMC] [PDF]
36. Kong SH, Ahn D, Kim BR, Srinivasan K, Ram S, Kim H, et al. A novel fracture prediction model using machine learning in a community-based cohort. JBMR Plus 2020;4:e10337.
[CROSSREF] [PUBMED] [PMC] [PDF]
37. Engels A, Reber KC, Lindlbauer I, Rapp K, Buchele G, Klenk J, et al. Osteoporotic hip fracture prediction from risk factors available in administrative claims data: a machine learning approach. PLoS One 2020;15:e0232969.
[CROSSREF] [PUBMED] [PMC]
38. Kalmet P, Sanduleanu S, Primakov S, Wu G, Jochems A, Refaee T, et al. Deep learning in fracture detection: a narrative review. Acta Orthop 2020;91:215-20.
[CROSSREF] [PUBMED] [PMC]