Osteoporosis: current diagnostic possibilities, a literature review
Department of General Surgery, Siemianowice Slaskie City Hospital, Siemianowice Slaskie, Poland
Department and Clinic of Internal Diseases, Diabetology and Nephrology, Metabolic Bone Diseases Unit, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
Menopause Rev 2026; 25(2)
Data publikacji online: 2026/08/03
Article file
- Narodowy Fundusz Zdrowia. NFZ o zdrowiu – osteoporoza 2013–2024. Warszawa, 2025. Centrala Narodowego Funduszu Zdrowia, Departament Analiz i Strategii. Available from: https://ezdrowie.gov.pl/portal/home/badania-i-dane/zdrowe-dane/raporty/nfz-o-zdrowiu-osteoporoza.
- Salari N, Ghasemi H, Mohammadi L, Behzadi MH, Rabieenia E, Shohaimi S, et al. The global prevalence of osteoporosis in the world: a comprehensive systematic review and meta-analysis. J Orthop Surg Res 2021; 16: 609.
- Cosman F, de Beur SJ, LeBoff MS, Lewiecki EM, Saag KG, Singer AJ, et al. Clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int 2014; 25: 2359-2381.
- Marra M, Sammarco R, Lorenzo AD, Iellamo F, Siervo M, Pietrobelli A, et al. Assessment of body composition in health and disease using bioelectrical impedance analysis (BIA) and dual energy x-ray absorptiometry (DXA): a critical overview. Contrast Media Mol Imaging 2019; 2019: 1-9.
- Al-Hashimi L, Klotsche J, Ohrndorf S, Gaber T, Hoff P. Trabecular bone score significantly influences treatment decisions in secondary osteoporosis. J Clin Med 2023; 12: 4147.
- Xu X, Li N, Li K, Li XY, Zhang P, Xuan YJ, et al. Discordance in diagnosis of osteoporosis by quantitative computed tomography and dual-energy x-ray absorptiometry in Chinese elderly men. J Orthop Transl 2019; 18: 59-64.
- Qadr H. Proportional counter in X-ray fluorescence. Aksaray University J Sci Eng 2021; 5: 1-7.
- Feng S, Lin S, Chiang Y, Lu MH, Chao YH. Deep learning-based hip X-ray image analysis for predicting osteoporosis. Appl Sci 2023; 14: 133.
- Alves-Silva EG, Fachetti Ribeiro B, Silva CF, de Kássia-Alves R, Arruda-Vasconcelos R, Louzada LM, et al. Evaluation of mandibular bone alterations by panoramic radiography: a potential tool in the identification of signs of osteopenia and osteoporosis. Bioengineering 2025; 12: 746.
- Schoenfeld E, Pekow P, Shieh M, Scales CD, Lagu T, Lindenauer PK. The diagnosis and management of patients with renal colic across a sample of US hospitals: high CT utilization despite low rates of admission and inpatient urologic intervention. PLoS One 2017; 12: e0169160.
- Young ES, Reed MJ, Pham TN, Gross JA, Taitsman LA, Kaplan SJ. Assessment of osteoporosis in injured older women admitted to a safety-net level one trauma center: a unique opportunity to fulfill an unmet need. Curr Gerontol Geriatr Res 2017; 2017: 4658050.
- Pickhardt PJ, Pooler BD, Lauder T, del Rio AM, Bruce RJ, Binkley N. Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med 2013; 158: 588-595.
- Hendrickson NR, Pickhardt PJ, Del Rio AM, Rosas HG, Anderson PA. Bone mineral density T-scores derived from CT attenuation numbers (Hounsfield units): clinical utility and correlation with dual-energy X-ray absorptiometry. Iowa Orthop J 2018; 38: 25-31.
- Lin W, He C, Xie F, Chen T, Zheng G, Yin H, et al. Quantitative CT screening improved lumbar BMD evaluation in older patients compared to dual-energy X-ray absorptiometry. BMC Geriatr 2023; 23: 231.
- American College of Radiology. ACR-SPR-SSR practice parameter for the performance of quantitative computed tomography (QCT) bone densitometry, 2014. Available from: https://gravitas.acr.org/PPTS/GetDocumentView?docId=11.
- Liu Z, Cheng Z, Ma C, Qi H, Yang ZH, Wu HY, et al. Automatic phantom-less QCT system with high precision of BMD measurement for osteoporosis screening: technique optimization and clinical validation. J Orthop Transl 2022; 33: 24-30.
- Tatsugami F, Higaki T, Nakamura Y, Honda Y, Awai K. Dual-energy CT: minimal essentials for radiologists. Jpn J Radiol 2022; 40: 547-559.
- Chen SB, Guo L, Zhao H, Wan X, Zang J. Quantitative measurements of dual-energy CT parameters in the diagnosis of osteoporosis – a highly sensitive and specific technique: an observational study. Medicine 2024; 103: e38559.
- Bakker C, Walker R, Besler B, Tse JJ, Manske SL, Martin CR, et al. A quantitative assessment of dual energy computed tomography‐based material decomposition for imaging bone marrow edema associated with acute knee injury. Med Phys 2021; 48: 1792-1803.
- Chen A, Feng S, Lai L, Yan CX. A meta-analysis of the value of MRI-based VBQ scores for evaluating osteoporosis. Bone Reports 2023; 19: 101711.
- Swinton PA, Elliott-Sale KJ, Sale C. Comparative analysis of bone outcomes between quantitative ultrasound and dual-energy X-ray absorptiometry from the UK Biobank cohort. Arch Osteoporos 2023; 18: 77.
- Yen CC, Lin WC, Wang TH, Chen GF, Chou DY, Lin DM, et al. Pre-screening for osteoporosis with calcaneus quantitative ultrasound and dual-energy X-ray absorptiometry bone density. Sci Rep 2021; 11: 11.
- Drozdzowska B, Pluskiewicz W. The ability of quantitative ultrasound at the calcaneus to identify postmenopausal women with different types of nontraumatic fractures. Ultrasound Med Biol 2002; 28: 1491-1497.
- Rolla M, Halupczok-Żyła J, Jawiarczyk-Przybyłowska A, Bolanowski M. Bone densitometry by radiofrequency echographic multi-spectrometry (REMS) in acromegaly patients. Endokrynol Pol 2020; 71: 524-531.
- Fuggle N, Reginster JY, Al-Daghri N, Bruyere O, Burlet N, Campusano C, et al. Radiofrequency echographic multi spectrometry (REMS) in the diagnosis and management of osteoporosis: state of the art. Aging Clin Exp Res 2024; 36: 135.
- Dean J. The deep learning revolution and its implications for computer architecture and chip design. In: 2020 IEEE International Solid-State Circuits Conference (ISSCC). Available from: https://arxiv.org/abs/1911.05289.
- Kruse C. The new possibilities from “big data” to overlooked associations between diabetes, biochemical parameters, glucose control, and osteoporosis. Curr Osteoporos Rep 2018; 16: 320-324.
- Chu P, Bo C, Liang X, Yang J, Megalooikonomou V, Yang F, et al. Using octuplet siamese network for osteoporosis analysis on dental panoramic radiographs. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2018: 2579-2582.
- Deniz CM, Xiang S, Hallyburton RS, Welbeck A, Babb JS, Honig S, et al. Segmentation of the proximal femur from mr images using deep convolutional neural networks. Sci Rep 2018; 8: 16485.
- Amani F, Amanzadeh M, Hamedan M, Amani P. Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis. BMC Musculoskelet Disord 2024; 25: 991.
Copyright: © 2026 Termedia Sp. z o. o. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.