This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Hernlund E, Svedbom A, Ivergård M, et al. Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA). Arch Osteoporos. 2013;8:136. https://doi.org/10.1007/s11657-013-0136-1Search in Google Scholar
Aibar-Almazán A, Voltes-Martínez A, Castellote-Caballero Y, et al. Current status of the diagnosis and management of osteoporosis. Int J Mol Sci. 2022;23:9465. https://doi.org/10.3390/ijms23169465Search in Google Scholar
Nethander M, Pettersson-Kymmer U, Vandenput L, et al. BMD-related genetic risk scores predict site-specific fractures as well as trabecular and cortical bone microstructure. J Clin Endocrinol Metab. 2020;105:e1344–e1357. https://doi.org/10.1210/clinem/dgaa082Search in Google Scholar
Shepherd JA, Schousboe JT, Broy SB, et al. Executive summary of the 2015 ISCD position development conference on advanced measures from DXA and QCT: fracture prediction beyond BMD. J Clin Densitom. 2015;18:274-286. https://doi.org/10.1016/j.jocd.2015.06.013Search in Google Scholar
Silva BC, Broy SB, Boutroy S, et al. Fracture risk prediction by non-BMD DXA measures: the 2015 ISCD official positions, part 2: trabecular bone score. J Clin Densitom. 2015;18:309-330. https://doi.org/10.1016/j.jocd.2015.06.008Search in Google Scholar
Adami G, Biffi A, Porcu G, et al. A systematic review on the performance of fracture risk assessment tools: FRAX, DeFRA, FRA-HS. J Endocrinol Invest. 2023;46:2287-2297. https://doi.org/10.1007/s40618-023-02082-8Search in Google Scholar
Soldati E, Rossi F, Vicente J, et al. Survey of MRI usefulness for the clinical assessment of bone microstructure. Int J Mol Sci. 2021;22:2509. https://doi.org/10.3390/ijms22052509Search in Google Scholar
Akhter MP, Recker RR. High resolution imaging in bone tissue research-review. Bone. 2021;143:115620. https://doi.org/10.1016/j.bone.2020.115620Search in Google Scholar
Johannesdottir F, Allaire B, Bouxsein ML. Fracture prediction by computed tomography and finite element analysis: current and future perspectives. Curr Osteoporos Rep. 2018;16:411-422. https://doi.org/10.1007/s11914-018-0450-z. Erratum in: Curr Osteoporos Rep. 2022;20:364. https://doi.org/10.1007/s11914-022-00724-zSearch in Google Scholar
Fleps I, Morgan EF. A Review of CT-based fracture risk assessment with finite element modeling and machine learning. Curr Osteoporos Rep. 2022;20:309-319. https://doi.org/10.1007/s11914-022-00743-wSearch in Google Scholar
Gebre RK, Hirvasniemi J, Lantto I, et al. Discrimination of low-energy acetabular fractures from controls using computed tomography-based bone characteristics. Ann Biomed Eng. 2021;49:367-381. https://doi.org/10.1007/s10439-020-02563-4Search in Google Scholar
Silva BC, Leslie WD, Resch H et al. Trabecular Bone Score: A noninvasive analytical method based upon the DXA image. J Bone Mineral Res. 2014;29:518-530. https://doi.org/10.1002/jbmr.3218Search in Google Scholar
López Picazo M, Humbert L, Di Gregorio S, et al. Discrimination of osteoporosis-related vertebral fractures by DXA-derived 3D measurements: a retrospective case-control study. Osteoporosis Int. 2019;30:1099-1110. https://doi.org/10.1007/s00198-019-04894-ySearch in Google Scholar
Xie Q, Chen Y, Hu Y, et al. Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography. BMC Med Imaging. 2022;22:140. https://doi.org/10.1186/s12880-022-00868-5Search in Google Scholar
Xue Z, Huo J, Sun X, et al. Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density. BMC Musculoskelet Disord. 2022;23:336. https://doi.org/10:1186/s12891-022-05309-6Search in Google Scholar
Yan J, Lai Y, Xu Y, et al. Editorial: Artificial intelligence-based medical image automatic diagnosis and prognosis prediction. Front Phys. 2023;11:1210010. https://doi.org/10.3389/fphy.2023.1210010Search in Google Scholar
Valentinitsch A, Trebeschi S, Kaesmacher J, et al. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int. 2019;30:1275-1285. https://doi.org/10.1007/s00198-019-04910-1Search in Google Scholar
Leonhardt Y, May P, Gordijenko O, et al. Opportunistic QCT bone mineral density measurements predicting osteoporotic fractures: a use case in a prospective clinical cohort. Front Endocrinol. 2020;11:586352. https://doi.org/10.3389/fendo.2020.586352Search in Google Scholar
Löffler MT, Jacob A, Valentinitsch A, et al. Improved prediction of incident vertebral fractures using opportunistic QCT compared to DXA. Eur Radiol. 2019;29:4980-4989. https://doi.org/10.1007/s00330-019-0618-wSearch in Google Scholar
Boutin RD, Hernandez AM, Lenchik L, et al. CT phantom evaluation of 67,392 American College of Radiology accreditation examinations: implications for opportunistic screening of osteoporosis using CT. Am J Roentgenol. 2021;216:447-452. https://doi.org/10.2214/AJR.20.22943Search in Google Scholar
Lenchik L, Weaver AA, Ward RJ, et al. Opportunistic screening for osteoporosis using computed tomography: State of the art and argument for paradigm shift. Curr Rheumatol Rep. 2018;20:74. https://doi.org/10.1007/s11926-018-0784-7Search in Google Scholar
Tatoń G, Rokita E, Rok T, et al. Oversampling in the computed tomography measurements applied for bone structure studies as a method of spatial resolution improvement. Pol J Radiol. 2012;77:14-18. https://doi.org/10.12659/pjr.882965Search in Google Scholar
Tatoń G, Rokita E, Wróbel A. Application of geometrical measurements in the assessment of vertebral strength. Pol J Radiol. 2013;78:15-18. https://doi.org/10.12659/PJR.883942Search in Google Scholar
Tatoń G, Rokita E, Wróbel et al. Combining areal DXA bone mineral density and vertebrae postero-anterior width improves the prediction of vertebral strength. Skeletal Radiol. 2013;42:1717-1725. https://doi.org/10.1007/s00256-013-1723-3Search in Google Scholar
Tatoń G, Rokita E, Korkosz et al. The ratio of anterior and posterior vertebral heights reinforces the utility of DXA in assessment of vertebrae strength. Calcif Tissue Int. 2014;95:112-121. https://doi.org/10.1007/s00223-014-9868-1Search in Google Scholar
Alswat KA. Gender disparities in osteoporosis. J Clin Med Res. 2017;9:382-387. https://doi.org/10.14740/jocmr2970wSearch in Google Scholar
Genant HK, Wu CY, Kuijk C, et al. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res. 1993;8:1137-1148. https://doi.org/10.1002/jbmr.5650080915Search in Google Scholar
Steiger JH. Tests for comparing elements of a correlation matrix. Psychol Bull. 1980;187:245-251. https://doi.org/10.1037/0033-2909.87.2.245Search in Google Scholar
Tabor Z, Rokita E. Comparison of trabecular bone architecture in young and old bone. Med Phys. 2000;27:765-772. https://doi.org/10.1118/1.598981Search in Google Scholar
Kubik T, Pasowicz M, Tabor Z, et al. Optimizing the assessment of age-related changes in trabecular bone. Phys Med Biol. 2002;47:1543-1553. https://doi.org/10.1088/0031-9155/47/9/309Search in Google Scholar
Tabor Z. Quantifying quality of trabecular bone from low-resolution images. Nalecz Institute of Bio-cybernetics and Biomedical Engineering Polish Academy of Science, Warsaw, 2009; 16-56.Search in Google Scholar
Karim L, Hussein AI, Morgan EF, et al. The mechanical behavior of bone. In: Marcus R, Feldman D, Dempster DW, Luckey M, Cauley JA, editors. Osteoporosis. Oxford: Academic Press; 2013. p. 431-452. https://doi.org/10.1016/B978-0-12-415853-5.00019-4Search in Google Scholar
Boskey AL, Imbert L. Bone quality changes associated with aging and disease: a review. Ann N Y Acad Sci. 2017;1410:93-106. https://doi.org/10.1111/nyas.13937Search in Google Scholar
Jain RK, Vokes T. Dual-energy X-ray absorptiometry. J Clin Densitometry. 2017;20:291-303. https://doi.org/10.1016/j.jocd.2017.06.014Search in Google Scholar
American College of Radiology. ACR-SPR-SSR practice guideline the performance of quantitative computed tomography (QCT) bone densitometry. 2013. Available at: https://www.acr.org/-/media/ACR/Files/Practice-Parameters/QCT.pdf?la=en (Accessed 12 April 2024).Search in Google Scholar
Yamada S, Chiba K, Okazaki N, et al. Correlation between vertebral bone microstructure and estimated strength in elderly women: an ex-vivo HR-pQCT study of cadaveric spine. Bone. 2019;120:459-464. https://doi.org/10.1016/j.bone.2018.12.005Search in Google Scholar
Liu Y, Wang L, Su Y, et al. CTXA hip: the effect of partial volume correction on volumetric bone mineral density data for cortical and trabecular bone. Arch Osteoporos. 2020;15:50. https://doi.org/10.1007/s11657-020-00721-8Search in Google Scholar
Engelke K. Quantitative computed tomography – current status and new developments. J Clin Densitom. 2017;20:309-321. https://doi.org/10.1016/j.jocd.2017.06.017Search in Google Scholar
Checefsky WA, Abidin AZ, Nagarajan MB, et al. Assessing vertebral fracture risk on volumetric quantitative computed tomography by geometric characterization of trabecular bone structure. Proc SPIE - Int Soc Opt Eng. 2016;9785:978508. https://doi.org/10.1117/12.2216898Search in Google Scholar
Lee DC, Hoffmann PF, Kopperdahl DL, et al. Phantomless calibration of CT scans for measurement of BMD and bone strength-inter-operator reanalysis precision. Bone. 2017;103:325-33. https://doi.org/10.1016/j.bone.2017.07.029Search in Google Scholar
Gibson LJ. Biomechanics of cellular solid. J Biomech. 2005;38:377-399. https://doi.org/10.1016/j.jbiomech.2004.09.027Search in Google Scholar
Moeendarbary E, Harris AR. Cell mechanics: principles, practices, and prospects. Rev Syst Biol Med. 2014;6:371-388. https://doi.org/10.1002/wsbm.1275Search in Google Scholar
Costanza G, Solaiyappan D, Tata ME. Properties, applications and recent developments of cellular solid materials: A review. Materials. 2023;16:7076. https://doi.org/10.3390/ma16227076Search in Google Scholar
Coulombe JC, Mullen ZK, Lynch ME, et al. Application of machine learning classifiers for microcomputed tomography data assessment of mouse bone microarchitecture. MethodsX. 2021;8:101497. https://doi.org/10.1016/jmex.2021.101497Search in Google Scholar
Kodama M, Takeuchi A, Uesugi M, at al. Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data. Energy and AI. 2023;100305. https://doi.org/10.1016/j.egyai.2023.100305Search in Google Scholar