{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:44:14Z","timestamp":1792111454590,"version":"4.4.0"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Skeletal Radiol"],"DOI":"10.1007\/s00256-026-05403-z","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T23:47:59Z","timestamp":1792108079000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-enhanced routine CT for opportunistic osteoporosis screening and risk assessment"],"prefix":"10.1007","author":[{"given":"Mengyao","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Run","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyang","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanwu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"key":"5403_CR1","doi-asserted-by":"publisher","first-page":"e75665","DOI":"10.2196\/75665","volume":"13","author":"J Liu","year":"2025","unstructured":"Liu J, Zhang L, Yuan Y, et al. Deep learning radiomics model based on computed tomography image for predicting the classification of osteoporotic vertebral fractures: algorithm development and validation. JMIR Med Inform. 2025;13:e75665. https:\/\/doi.org\/10.2196\/75665.","journal-title":"JMIR Med Inform"},{"key":"5403_CR2","doi-asserted-by":"publisher","first-page":"1699762","DOI":"10.3389\/frai.2025.1699762","volume":"8","author":"W Zhang","year":"2025","unstructured":"Zhang W, Tai D, Kang S, Li K. The application progress of artificial intelligence in osteoporosis diagnosis. Front Artif Intell. 2025;8:1699762. https:\/\/doi.org\/10.3389\/frai.2025.1699762.","journal-title":"Front Artif Intell"},{"key":"5403_CR3","doi-asserted-by":"publisher","first-page":"1582960","DOI":"10.3389\/frai.2025.1582960","volume":"8","author":"M Guo","year":"2025","unstructured":"Guo M, Zhang Y, Gu X, et al. A comparative study of bone density in elderly people measured with AI and QCT. Front Artif Intell. 2025;8:1582960. https:\/\/doi.org\/10.3389\/frai.2025.1582960.","journal-title":"Front Artif Intell"},{"issue":"7","key":"5403_CR4","doi-asserted-by":"publisher","first-page":"4296","DOI":"10.1002\/mp.16230","volume":"50","author":"A Tariq","year":"2023","unstructured":"Tariq A, Patel BN, Sensakovic WF, Fahrenholtz SJ, Banerjee I. Opportunistic screening for low bone density using abdominopelvic computed tomography scans. Med Phys. 2023;50(7):4296\u2013307. https:\/\/doi.org\/10.1002\/mp.16230.","journal-title":"Med Phys"},{"issue":"10","key":"5403_CR5","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1007\/s00198-024-07179-1","volume":"35","author":"A Paderno","year":"2024","unstructured":"Paderno A, Ataide Gomes EJ, Gilberg L, et al. Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping review. Osteoporos Int. 2024;35(10):1681\u201392. https:\/\/doi.org\/10.1007\/s00198-024-07179-1.","journal-title":"Osteoporos Int"},{"key":"5403_CR6","doi-asserted-by":"publisher","DOI":"10.1177\/1759720x211024029","volume":"13","author":"V Aggarwal","year":"2021","unstructured":"Aggarwal V, Maslen C, Abel RL, et al. Opportunistic diagnosis of osteoporosis, fragile bone strength and vertebral fractures from routine CT scans; a review of approved technology systems and pathways to implementation. Ther Adv Musculoskelet Dis. 2021;13:1759720x211024029. https:\/\/doi.org\/10.1177\/1759720x211024029.","journal-title":"Ther Adv Musculoskelet Dis"},{"key":"5403_CR7","doi-asserted-by":"publisher","first-page":"e77155","DOI":"10.2196\/77155","volume":"27","author":"A Wang","year":"2025","unstructured":"Wang A, Ma Z, Wang T, et al. Deep learning-assisted automated diagnosis of osteoporosis based on computed tomography scans: systematic review and meta-analysis. J Med Internet Res. 2025;27:e77155. https:\/\/doi.org\/10.2196\/77155.","journal-title":"J Med Internet Res"},{"issue":"1","key":"5403_CR8","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s00223-025-01377-7","volume":"116","author":"H Ya'nan","year":"2025","unstructured":"Ya\u2019nan H, Jianfeng Z, Wei T, Jianfeng Y, Zhenhua Z. Accuracy of low-dose chest CT-based artificial intelligence models in osteoporosis detection: a systematic review and meta-analysis. Calcif Tissue Int. 2025;116(1):67. https:\/\/doi.org\/10.1007\/s00223-025-01377-7.","journal-title":"Calcif Tissue Int"},{"issue":"4","key":"5403_CR9","doi-asserted-by":"publisher","first-page":"1831","DOI":"10.1007\/s00330-020-07312-8","volume":"31","author":"Y Fang","year":"2021","unstructured":"Fang Y, Li W, Chen X, et al. Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks. Eur Radiol. 2021;31(4):1831\u201342. https:\/\/doi.org\/10.1007\/s00330-020-07312-8.","journal-title":"Eur Radiol"},{"issue":"3","key":"5403_CR10","doi-asserted-by":"publisher","first-page":"500","DOI":"10.3803\/EnM.2023.1860","volume":"39","author":"J Oh","year":"2024","unstructured":"Oh J, Kim B, Oh G, Hwangbo Y, Ye JC. End-to-end semi-supervised opportunistic osteoporosis screening using computed tomography. Endocrinol Metab (Seoul). 2024;39(3):500\u201310. https:\/\/doi.org\/10.3803\/EnM.2023.1860.","journal-title":"Endocrinol Metab (Seoul)"},{"key":"5403_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrai.2025.100025","volume":"3","author":"Y Que","year":"2025","unstructured":"Que Y, Xia X, Luo X, Deng Y, Chen M. Diagnostic performance of an artificial intelligence based software for opportunistic osteoporosis detection using chest CT. Eur J Radiol Artif Intell. 2025;3:100025. https:\/\/doi.org\/10.1016\/j.ejrai.2025.100025.","journal-title":"Eur J Radiol Artif Intell"},{"issue":"1","key":"5403_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3171\/2025.1.Spine24900","volume":"43","author":"E Feng","year":"2025","unstructured":"Feng E, Jayasuriya NM, Nathani KR, et al. Artificial intelligence image analysis for Hounsfield units in preoperative thoracolumbar CT scans: an automated screening for osteoporosis in patients undergoing spine surgery. J Neurosurg Spine. 2025;43(1):1\u20138. https:\/\/doi.org\/10.3171\/2025.1.Spine24900.","journal-title":"J Neurosurg Spine"},{"issue":"1","key":"5403_CR13","doi-asserted-by":"publisher","first-page":"9697","DOI":"10.1038\/s41598-021-89111-9","volume":"11","author":"L Folle","year":"2021","unstructured":"Folle L, Meinderink T, Simon D, et al. Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density. Sci Rep. 2021;11(1):9697. https:\/\/doi.org\/10.1038\/s41598-021-89111-9.","journal-title":"Sci Rep"},{"key":"5403_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbo.2024.100641","volume":"49","author":"Z Wang","year":"2024","unstructured":"Wang Z, Tan Y, Zeng K, Tan H, Xiao P, Su G. Bone density measurement in patients with spinal metastatic tumors using chest quantitative CT deep learning model. J Bone Oncol. 2024;49:100641. https:\/\/doi.org\/10.1016\/j.jbo.2024.100641.","journal-title":"J Bone Oncol"},{"key":"5403_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2023.1207949","volume":"14","author":"J Bodden","year":"2023","unstructured":"Bodden J, Dieckmeyer M, Sollmann N, et al. Incidental vertebral fracture prediction using neuronal network-based automatic spine segmentation and volumetric bone mineral density extraction from routine clinical CT scans. Front Endocrinol (Lausanne). 2023;14:1207949. https:\/\/doi.org\/10.3389\/fendo.2023.1207949.","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"1","key":"5403_CR16","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s00198-023-06900-w","volume":"35","author":"T Peng","year":"2024","unstructured":"Peng T, Zeng X, Li Y, et al. A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening. Osteoporos Int. 2024;35(1):117\u201328. https:\/\/doi.org\/10.1007\/s00198-023-06900-w.","journal-title":"Osteoporos Int"},{"issue":"1","key":"5403_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-45824-7","volume":"14","author":"S Oh","year":"2024","unstructured":"Oh S, Kang WY, Park H, et al. Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening. Sci Rep. 2024;14(1):363. https:\/\/doi.org\/10.1038\/s41598-023-45824-7.","journal-title":"Sci Rep"},{"issue":"1","key":"5403_CR18","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1186\/s41747-024-00483-9","volume":"8","author":"J Bodden","year":"2024","unstructured":"Bodden J, Prucker P, Sekuboyina A, et al. Reproducibility of CT-based opportunistic vertebral volumetric bone mineral density measurements from an automated segmentation framework. Eur Radiol Exp. 2024;8(1):86. https:\/\/doi.org\/10.1186\/s41747-024-00483-9.","journal-title":"Eur Radiol Exp"},{"issue":"3","key":"5403_CR19","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1007\/s00330-021-08284-z","volume":"32","author":"S R\u00fchling","year":"2022","unstructured":"R\u00fchling S, Navarro F, Sekuboyina A, et al. Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements. Eur Radiol. 2022;32(3):1465\u201374. https:\/\/doi.org\/10.1007\/s00330-021-08284-z.","journal-title":"Eur Radiol"},{"issue":"1152","key":"5403_CR20","doi-asserted-by":"publisher","first-page":"20230047","DOI":"10.1259\/bjr.20230047","volume":"96","author":"Y Pan","year":"2023","unstructured":"Pan Y, Zhao F, Cheng G, et al. Automated vertebral bone mineral density measurement with phantomless internal calibration in chest LDCT scans using deep learning. Br J Radiol. 2023;96(1152):20230047. https:\/\/doi.org\/10.1259\/bjr.20230047.","journal-title":"Br J Radiol"},{"issue":"2","key":"5403_CR21","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.250917","volume":"317","author":"M Westerhoff","year":"2025","unstructured":"Westerhoff M, Gyftopoulos S, Dane B, et al. Deep learning-based opportunistic CT osteoporosis screening and the establishment of normative values. Radiology. 2025;317(2):e250917. https:\/\/doi.org\/10.1148\/radiol.250917.","journal-title":"Radiology"},{"issue":"1","key":"5403_CR22","doi-asserted-by":"publisher","first-page":"25014","DOI":"10.1038\/s41598-024-73709-w","volume":"14","author":"H Park","year":"2024","unstructured":"Park H, Kang WY, Woo OH, Lee J, Yang Z, Oh S. Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners. Sci Rep. 2024;14(1):25014. https:\/\/doi.org\/10.1038\/s41598-024-73709-w.","journal-title":"Sci Rep"},{"issue":"1","key":"5403_CR23","doi-asserted-by":"publisher","first-page":"5449","DOI":"10.1038\/s41598-025-88551-x","volume":"15","author":"BE Matheson","year":"2025","unstructured":"Matheson BE, Boyd SK. Establishing the effect of computed tomography reconstruction kernels on the measure of bone mineral density in opportunistic osteoporosis screening. Sci Rep. 2025;15(1):5449. https:\/\/doi.org\/10.1038\/s41598-025-88551-x.","journal-title":"Sci Rep"},{"issue":"4","key":"5403_CR24","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1002\/jcsm.13487","volume":"15","author":"SW Cho","year":"2024","unstructured":"Cho SW, Baek S, Han S, et al. Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults. J Cachexia Sarcopenia Muscle. 2024;15(4):1418\u201329. https:\/\/doi.org\/10.1002\/jcsm.13487.","journal-title":"J Cachexia Sarcopenia Muscle"},{"issue":"3","key":"5403_CR25","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1016\/j.acra.2023.08.030","volume":"31","author":"S Wang","year":"2024","unstructured":"Wang S, Tong X, Fan Y, et al. Combining deep learning and radiomics for automated, objective, comprehensive bone mineral density assessment from low-dose chest computed tomography. Acad Radiol. 2024;31(3):1180\u20138. https:\/\/doi.org\/10.1016\/j.acra.2023.08.030.","journal-title":"Acad Radiol"},{"key":"5403_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2025.117456","volume":"195","author":"K Uemura","year":"2025","unstructured":"Uemura K, Kono S, Takashima K, et al. Side-to-side differences in hip bone mineral density in patients with unilateral hip osteoarthritis. Bone. 2025;195:117456. https:\/\/doi.org\/10.1016\/j.bone.2025.117456.","journal-title":"Bone"},{"key":"5403_CR27","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1016\/j.jare.2026.01.037","volume":"88","author":"Z Guan","year":"2026","unstructured":"Guan Z, Wang Y, Zhang S, et al. Construction of musculoskeletal quantitative model based on deep learning and study of musculoskeletal relationship in patients with osteoporosis. J Adv Res. 2026;88:899\u2013909. https:\/\/doi.org\/10.1016\/j.jare.2026.01.037.","journal-title":"J Adv Res"},{"issue":"3","key":"5403_CR28","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1097\/rct.0000000000001437","volume":"47","author":"K Yoshida","year":"2023","unstructured":"Yoshida K, Tanabe Y, Nishiyama H, et al. Feasibility of bone mineral density and bone microarchitecture assessment using deep learning with a convolutional neural network. J Comput Assist Tomogr. 2023;47(3):467\u201374. https:\/\/doi.org\/10.1097\/rct.0000000000001437.","journal-title":"J Comput Assist Tomogr"},{"key":"5403_CR29","doi-asserted-by":"publisher","first-page":"1061911","DOI":"10.3389\/fphys.2022.1061911","volume":"13","author":"JW Kang","year":"2022","unstructured":"Kang JW, Park C, Lee DE, Yoo JH, Kim M. Prediction of bone mineral density in CT using deep learning with explainability. Front Physiol. 2022;13:1061911. https:\/\/doi.org\/10.3389\/fphys.2022.1061911.","journal-title":"Front Physiol"},{"issue":"4","key":"5403_CR30","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1016\/j.acra.2024.10.049","volume":"32","author":"M Mohammadi-Sadr","year":"2025","unstructured":"Mohammadi-Sadr M, Cheki M, Moslehi M, Zarasvandnia M, Salamat MR. A novel approach based on integrating radiomics, bone morphometry and Hounsfield unit-derived from routine chest CT for bone mineral density assessment. Acad Radiol. 2025;32(4):2284\u201396. https:\/\/doi.org\/10.1016\/j.acra.2024.10.049.","journal-title":"Acad Radiol"},{"issue":"1","key":"5403_CR31","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11657-024-01455-7","volume":"19","author":"J Pan","year":"2024","unstructured":"Pan J, Lin PC, Gong SC, et al. Feasibility study of opportunistic osteoporosis screening on chest CT using a multi-feature fusion DCNN model. Arch Osteoporos. 2024;19(1):98. https:\/\/doi.org\/10.1007\/s11657-024-01455-7.","journal-title":"Arch Osteoporos"},{"issue":"1","key":"5403_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11010050","volume":"11","author":"X Tong","year":"2024","unstructured":"Tong X, Wang S, Zhang J, Fan Y, Liu Y, Wei W. Automatic osteoporosis screening system using radiomics and deep learning from low-dose chest CT images. Bioengineering (Basel). 2024;11(1):50. https:\/\/doi.org\/10.3390\/bioengineering11010050.","journal-title":"Bioengineering (Basel)"},{"issue":"7","key":"5403_CR33","doi-asserted-by":"publisher","first-page":"5097","DOI":"10.1007\/s00330-023-09421-6","volume":"33","author":"YC Chen","year":"2023","unstructured":"Chen YC, Li YT, Kuo PC, et al. Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography. Eur Radiol. 2023;33(7):5097\u2013106. https:\/\/doi.org\/10.1007\/s00330-023-09421-6.","journal-title":"Eur Radiol"},{"issue":"1","key":"5403_CR34","doi-asserted-by":"publisher","DOI":"10.1186\/s12891-025-08631-x","volume":"26","author":"C Du","year":"2025","unstructured":"Du C, He J, Cheng Q, et al. Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT. BMC Musculoskelet Disord. 2025;26(1):378. https:\/\/doi.org\/10.1186\/s12891-025-08631-x.","journal-title":"BMC Musculoskelet Disord"},{"issue":"5","key":"5403_CR35","doi-asserted-by":"publisher","first-page":"2003","DOI":"10.1016\/j.acra.2023.10.035","volume":"31","author":"X Yuan","year":"2024","unstructured":"Yuan X, Liang Y, Yang H, et al. Applying machine learning analysis based on proximal femur of abdominal computed tomography to screen for abnormal bone mass in femur. Acad Radiol. 2024;31(5):2003\u201310. https:\/\/doi.org\/10.1016\/j.acra.2023.10.035.","journal-title":"Acad Radiol"},{"key":"5403_CR36","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.2147\/cia.S538123","volume":"20","author":"H Yang","year":"2025","unstructured":"Yang H, Li J, Zheng X, et al. Unsupervised clustering of vertebral Hounsfield units in opportunistic chest CT for stratifying bone mass subtypes in a 2 years\u2019 period. Clin Interv Aging. 2025;20:1561\u20139. https:\/\/doi.org\/10.2147\/cia.S538123.","journal-title":"Clin Interv Aging"},{"issue":"6","key":"5403_CR37","doi-asserted-by":"publisher","first-page":"3519","DOI":"10.1007\/s00330-024-11263-9","volume":"35","author":"Y Kim","year":"2025","unstructured":"Kim Y, Kim HY, Lee S, Hong S, Lee JW. Age-dependent changes in CT vertebral attenuation values in opportunistic screening for osteoporosis: a nationwide multi-center study. Eur Radiol. 2025;35(6):3519\u201327. https:\/\/doi.org\/10.1007\/s00330-024-11263-9.","journal-title":"Eur Radiol"},{"key":"5403_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107916","volume":"170","author":"K Zhang","year":"2024","unstructured":"Zhang K, Lin PC, Pan J, et al. DeepmdQCT: a multitask network with domain invariant features and comprehensive attention mechanism for quantitative computer tomography diagnosis of osteoporosis. Comput Biol Med. 2024;170:107916. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107916.","journal-title":"Comput Biol Med"},{"issue":"2","key":"5403_CR39","doi-asserted-by":"publisher","DOI":"10.1093\/jbmrpl\/ziaf191","volume":"10","author":"AM Auriat","year":"2025","unstructured":"Auriat AM, Patel M, Zhang L, et al. Evaluating an artificial intelligence software for opportunistic low bone mineral density and osteoporosis screening: a validation study. JBMR Plus. 2025;10(2):ziaf191. https:\/\/doi.org\/10.1093\/jbmrpl\/ziaf191.","journal-title":"JBMR Plus"},{"key":"5403_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2024.111521","volume":"177","author":"X Tong","year":"2024","unstructured":"Tong X, Wang S, Cheng Q, et al. Effect of fully automatic classification model from different tube voltage images on bone density screening: a self-controlled study. Eur J Radiol. 2024;177:111521. https:\/\/doi.org\/10.1016\/j.ejrad.2024.111521.","journal-title":"Eur J Radiol"},{"issue":"4","key":"5403_CR41","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.240300","volume":"6","author":"AS Tejani","year":"2024","unstructured":"Tejani AS, Klontzas ME, Gatti AA, et al. Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiol Artif Intell. 2024;6(4):e240300. https:\/\/doi.org\/10.1148\/ryai.240300.","journal-title":"Radiol Artif Intell"},{"issue":"10","key":"5403_CR42","doi-asserted-by":"publisher","first-page":"3283","DOI":"10.1038\/s41591-025-03953-8","volume":"31","author":"V Sounderajah","year":"2025","unstructured":"Sounderajah V, Guni A, Liu X, et al. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence. Nat Med. 2025;31(10):3283\u20139. https:\/\/doi.org\/10.1038\/s41591-025-03953-8.","journal-title":"Nat Med"},{"issue":"5","key":"5403_CR43","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222044","volume":"307","author":"PJ Pickhardt","year":"2023","unstructured":"Pickhardt PJ, Summers RM, Garrett JW, et al. Opportunistic screening: Radiology Scientific Expert Panel. Radiology. 2023;307(5):e222044. https:\/\/doi.org\/10.1148\/radiol.222044.","journal-title":"Radiology"},{"issue":"5","key":"5403_CR44","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1007\/s00198-026-08067-6","volume":"37","author":"CF Huang","year":"2026","unstructured":"Huang CF, Fang WH, Chen KH, et al. Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region. Osteoporos Int. 2026;37(5):1039\u201354. https:\/\/doi.org\/10.1007\/s00198-026-08067-6.","journal-title":"Osteoporos Int"},{"issue":"5","key":"5403_CR45","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1093\/jbmr\/zjaf050","volume":"40","author":"N Hong","year":"2025","unstructured":"Hong N, Cho SW, Lee YH, et al. Deep learning-based identification of vertebral fracture and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment to predict incident fracture. J Bone Miner Res. 2025;40(5):628\u201338. https:\/\/doi.org\/10.1093\/jbmr\/zjaf050.","journal-title":"J Bone Miner Res"},{"issue":"12","key":"5403_CR46","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1002\/jbmr.4916","volume":"38","author":"J Nicolaes","year":"2023","unstructured":"Nicolaes J, Skj\u00f8dt MK, Raeymaeckers S, et al. Towards improved identification of vertebral fractures in routine computed tomography (CT) scans: development and external validation of a machine learning algorithm. J Bone Miner Res. 2023;38(12):1856\u201366. https:\/\/doi.org\/10.1002\/jbmr.4916.","journal-title":"J Bone Miner Res"},{"issue":"9","key":"5403_CR47","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1002\/jbmr.4808","volume":"38","author":"DE Whittier","year":"2023","unstructured":"Whittier DE, Samelson EJ, Hannan MT, et al. A fracture risk assessment tool for high resolution peripheral quantitative computed tomography. J Bone Miner Res. 2023;38(9):1234\u201344. https:\/\/doi.org\/10.1002\/jbmr.4808.","journal-title":"J Bone Miner Res"},{"issue":"1","key":"5403_CR48","doi-asserted-by":"publisher","first-page":"38016","DOI":"10.1038\/s41598-025-21894-7","volume":"15","author":"B Zheng","year":"2025","unstructured":"Zheng B, Yu P, Zhu Z, Liang Y, Liu H. Machine learning integration of multi-modal radiomics and clinical factors predicts refracture risk after percutaneous kyphoplasty in postmenopausal women. Sci Rep. 2025;15(1):38016. https:\/\/doi.org\/10.1038\/s41598-025-21894-7.","journal-title":"Sci Rep"},{"issue":"Pt B","key":"5403_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110307","volume":"192","author":"JI Yoo","year":"2025","unstructured":"Yoo JI, Kim HS, Kim DY, Byun DW, Ha YC, Lee YK. Individual thigh muscle and proximal femoral features predict displacement in femoral neck fractures: an AI-driven CT analysis. Comput Biol Med. 2025;192(Pt B):110307. https:\/\/doi.org\/10.1016\/j.compbiomed.2025.110307.","journal-title":"Comput Biol Med"},{"issue":"13","key":"5403_CR50","doi-asserted-by":"publisher","first-page":"3329","DOI":"10.7150\/ijms.114419","volume":"22","author":"C Ge","year":"2025","unstructured":"Ge C, Li C, Zhu Y, Yang C, Xu X. Comparison of artificial intelligence models using CT radiomics for predicting post-vertebral augmentation residual back pain in osteoporotic vertebral compression fractures. Int J Med Sci. 2025;22(13):3329\u201341. https:\/\/doi.org\/10.7150\/ijms.114419.","journal-title":"Int J Med Sci"},{"issue":"4","key":"5403_CR51","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.1007\/s00330-024-11077-9","volume":"35","author":"J Singh","year":"2025","unstructured":"Singh J, Kokenberger G, Pu L, et al. Predicting post-lung transplant survival in systemic sclerosis using CT-derived features from preoperative chest CT scans. Eur Radiol. 2025;35(4):2005\u201317. https:\/\/doi.org\/10.1007\/s00330-024-11077-9.","journal-title":"Eur Radiol"},{"issue":"3","key":"5403_CR52","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.jacr.2025.01.004","volume":"22","author":"K Yatim","year":"2025","unstructured":"Yatim K, Ribas GT, Elton DC, et al. Applying artificial intelligence to quantify body composition on abdominal CTs and better predict kidney transplantation wait-list mortality. J Am Coll Radiol. 2025;22(3):332\u201341. https:\/\/doi.org\/10.1016\/j.jacr.2025.01.004.","journal-title":"J Am Coll Radiol"},{"issue":"2","key":"5403_CR53","doi-asserted-by":"publisher","DOI":"10.1136\/rmdopen-2024-005090","volume":"11","author":"MM Sieren","year":"2025","unstructured":"Sieren MM, Grasshoff H, Riemekasten G, et al. Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis. RMD Open. 2025;11(2):e005090. https:\/\/doi.org\/10.1136\/rmdopen-2024-005090.","journal-title":"RMD Open"},{"issue":"4","key":"5403_CR54","doi-asserted-by":"publisher","DOI":"10.1093\/gerona\/glae055","volume":"79","author":"AD Weston","year":"2024","unstructured":"Weston AD, Grossardt BR, Garner HW, et al. Abdominal body composition reference ranges and association with chronic conditions in an age- and sex-stratified representative sample of a geographically defined American population. J Gerontol A Biol Sci Med Sci. 2024;79(4):glae055. https:\/\/doi.org\/10.1093\/gerona\/glae055.","journal-title":"J Gerontol A Biol Sci Med Sci"},{"issue":"6","key":"5403_CR55","doi-asserted-by":"publisher","first-page":"e2474","DOI":"10.1210\/clinem\/dgac083","volume":"107","author":"SW Burm","year":"2022","unstructured":"Burm SW, Hong N, Lee S, et al. Preoperative thoracic muscle mass predicts bone density change after parathyroidectomy in primary hyperparathyroidism. J Clin Endocrinol Metab. 2022;107(6):e2474\u201380. https:\/\/doi.org\/10.1210\/clinem\/dgac083.","journal-title":"J Clin Endocrinol Metab"},{"issue":"6","key":"5403_CR56","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1016\/j.mayocp.2023.09.021","volume":"99","author":"AD Rule","year":"2024","unstructured":"Rule AD, Grossardt BR, Weston AD, et al. Older tissue age derived from abdominal computed tomography biomarkers of muscle, fat, and bone is associated with chronic conditions and higher mortality. Mayo Clin Proc. 2024;99(6):878\u201390. https:\/\/doi.org\/10.1016\/j.mayocp.2023.09.021.","journal-title":"Mayo Clin Proc"},{"issue":"3","key":"5403_CR57","doi-asserted-by":"publisher","first-page":"4369","DOI":"10.1007\/s11357-025-01589-7","volume":"47","author":"M Sim","year":"2025","unstructured":"Sim M, Gebre AK, Dalla Via J, et al. Automated abdominal aortic calcification scoring from vertebral fracture assessment images and fall-associated hospitalisations: the Manitoba Bone Mineral Density Registry. Geroscience. 2025;47(3):4369\u201380. https:\/\/doi.org\/10.1007\/s11357-025-01589-7.","journal-title":"Geroscience"},{"issue":"3","key":"5403_CR58","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1093\/jbmr\/zjae208","volume":"40","author":"C Smith","year":"2025","unstructured":"Smith C, Sim M, Ilyas Z, et al. Automated abdominal aortic calcification and major adverse cardiovascular events in people undergoing osteoporosis screening: the Manitoba Bone Mineral Density Registry. J Bone Miner Res. 2025;40(3):323\u201331. https:\/\/doi.org\/10.1093\/jbmr\/zjae208.","journal-title":"J Bone Miner Res"},{"issue":"12","key":"5403_CR59","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1002\/jbmr.4921","volume":"38","author":"J Dalla Via","year":"2023","unstructured":"Dalla Via J, Gebre AK, Smith C, et al. Machine-learning assessed abdominal aortic calcification is associated with long-term fall and fracture risk in community-dwelling older Australian women. J Bone Miner Res. 2023;38(12):1867\u201376. https:\/\/doi.org\/10.1002\/jbmr.4921.","journal-title":"J Bone Miner Res"},{"issue":"4","key":"5403_CR60","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1016\/j.jacr.2023.05.006","volume":"21","author":"M Naghavi","year":"2024","unstructured":"Naghavi M, Atlas K, Jaberzadeh A, et al. Validation of opportunistic artificial intelligence-based bone mineral density measurements in coronary artery calcium scans. J Am Coll Radiol. 2024;21(4):624\u201332. https:\/\/doi.org\/10.1016\/j.jacr.2023.05.006.","journal-title":"J Am Coll Radiol"},{"key":"5403_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2025.106085","volume":"163","author":"SD Ganz","year":"2025","unstructured":"Ganz SD. Fully-guided placement of dental implants utilizing nasopalatine canal fixation in a novel rotational path surgical template design: a retrospective case series. J Dent. 2025;163:106085. https:\/\/doi.org\/10.1016\/j.jdent.2025.106085.","journal-title":"J Dent"},{"issue":"5","key":"5403_CR62","doi-asserted-by":"publisher","first-page":"349","DOI":"10.5051\/jpis.2302880144","volume":"54","author":"JH Lee","year":"2024","unstructured":"Lee JH, Yun JH, Kim YT. Deep learning to assess bone quality from panoramic radiographs: the feasibility of clinical application through comparison with an implant surgeon and cone-beam computed tomography. J Periodontal Implant Sci. 2024;54(5):349\u201358. https:\/\/doi.org\/10.5051\/jpis.2302880144.","journal-title":"J Periodontal Implant Sci"},{"issue":"1","key":"5403_CR63","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-16074-w","volume":"12","author":"Y Xiao","year":"2022","unstructured":"Xiao Y, Liang Q, Zhou L, et al. Construction of a new automatic grading system for jaw bone mineral density level based on deep learning using cone beam computed tomography. Sci Rep. 2022;12(1):12841. https:\/\/doi.org\/10.1038\/s41598-022-16074-w.","journal-title":"Sci Rep"},{"key":"5403_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2025.103131","volume":"165","author":"L Marsilio","year":"2025","unstructured":"Marsilio L, Marzorati D, Rossi M, et al. Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint clinical assessment in shoulder CT scans. Artif Intell Med. 2025;165:103131. https:\/\/doi.org\/10.1016\/j.artmed.2025.103131.","journal-title":"Artif Intell Med"},{"issue":"1","key":"5403_CR65","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-19698-w","volume":"15","author":"A Lagzouli","year":"2025","unstructured":"Lagzouli A, Evans L, Hopkinson M, et al. A deep learning-based framework for standardized analysis of trabecular bone compartments from micro-CT imaging data in the mouse tibia. Sci Rep. 2025;15(1):35807. https:\/\/doi.org\/10.1038\/s41598-025-19698-w.","journal-title":"Sci Rep"},{"issue":"1","key":"5403_CR66","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1038\/s41413-021-00156-y","volume":"9","author":"E Kague","year":"2021","unstructured":"Kague E, Turci F, Newman E, et al. 3D assessment of intervertebral disc degeneration in zebrafish identifies changes in bone density that prime disc disease. Bone Res. 2021;9(1):39. https:\/\/doi.org\/10.1038\/s41413-021-00156-y.","journal-title":"Bone Res"},{"issue":"1","key":"5403_CR67","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01240-5","volume":"24","author":"J Liu","year":"2024","unstructured":"Liu J, Wang H, Shan X, et al. Hybrid transformer convolutional neural network-based radiomics models for osteoporosis screening in routine CT. BMC Med Imaging. 2024;24(1):62. https:\/\/doi.org\/10.1186\/s12880-024-01240-5.","journal-title":"BMC Med Imaging"},{"key":"5403_CR68","doi-asserted-by":"publisher","unstructured":"Huang J, Guo H, Lu L, et al. Opportunistic osteoporosis diagnosis via texture-preserving self-supervision, mixture of experts and multi-task integration. Cham: Springer Nature Switzerland; 2025. p. 428\u201338. https:\/\/doi.org\/10.1007\/978-3-032-05182-0_42.","DOI":"10.1007\/978-3-032-05182-0_42"},{"key":"5403_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110642","volume":"158","author":"F Poullain","year":"2023","unstructured":"Poullain F, Champsaur P, Pauly V, et al. Vertebral trabecular bone texture analysis in opportunistic MRI and CT scan can distinguish patients with and without osteoporotic vertebral fracture: a preliminary study. Eur J Radiol. 2023;158:110642. https:\/\/doi.org\/10.1016\/j.ejrad.2022.110642.","journal-title":"Eur J Radiol"},{"issue":"10","key":"5403_CR70","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1007\/s40618-022-01837-z","volume":"45","author":"E Biamonte","year":"2022","unstructured":"Biamonte E, Levi R, Carrone F, et al. Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures. J Endocrinol Invest. 2022;45(10):2007\u201317. https:\/\/doi.org\/10.1007\/s40618-022-01837-z.","journal-title":"J Endocrinol Invest"},{"issue":"1","key":"5403_CR71","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1186\/s12877-022-03502-9","volume":"22","author":"CB Huang","year":"2022","unstructured":"Huang CB, Hu JS, Tan K, Zhang W, Xu TH, Yang L. Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study. BMC Geriatr. 2022;22(1):796. https:\/\/doi.org\/10.1186\/s12877-022-03502-9.","journal-title":"BMC Geriatr"},{"issue":"4","key":"5403_CR72","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12040923","volume":"12","author":"H Park","year":"2022","unstructured":"Park H, Lee SY, Lee J, et al. Detecting multiple myeloma infiltration of the bone marrow on CT scans in patients with osteopenia: feasibility of radiomics analysis. Diagnostics (Basel). 2022;12(4):923. https:\/\/doi.org\/10.3390\/diagnostics12040923.","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"5403_CR73","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/s41747-025-00572-3","volume":"9","author":"FM Ulivieri","year":"2025","unstructured":"Ulivieri FM, Messina C, Vitale FM, Rinaudo L, Grossi E. Artificial intelligence for predicting the risk of bone fragility fractures in osteoporosis. Eur Radiol Exp. 2025;9(1):62. https:\/\/doi.org\/10.1186\/s41747-025-00572-3.","journal-title":"Eur Radiol Exp"},{"key":"5403_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2025.117745","volume":"204","author":"Y Sugawara","year":"2026","unstructured":"Sugawara Y, Shimizu T, Ishizu H, et al. Development of an explainable machine learning model to reproduce and interpret expert pharmacological decisions in osteoporosis treatment. Bone. 2026;204:117745. https:\/\/doi.org\/10.1016\/j.bone.2025.117745.","journal-title":"Bone"},{"issue":"12","key":"5403_CR75","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70336","volume":"26","author":"Y Iwao","year":"2025","unstructured":"Iwao Y, Shiotsuki K, Hashimoto F, et al. An exploratory study of explainable deep learning for predicting bone mineral density using clavicle features on chest radiographs: a multi-task approach with regression and segmentation. J Appl Clin Med Phys. 2025;26(12):e70336. https:\/\/doi.org\/10.1002\/acm2.70336.","journal-title":"J Appl Clin Med Phys"},{"key":"5403_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2025.117592","volume":"200","author":"X Yang","year":"2025","unstructured":"Yang X, Liu J, Huang X, et al. Machine learning-driven clinical decision support for low bone mineral density: a web-based prediction model with explainable AI integration. Bone. 2025;200:117592. https:\/\/doi.org\/10.1016\/j.bone.2025.117592.","journal-title":"Bone"},{"key":"5403_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2025.105889","volume":"199","author":"W Jin","year":"2025","unstructured":"Jin W, Xu L, Yue C, et al. Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records. Int J Med Inform. 2025;199:105889. https:\/\/doi.org\/10.1016\/j.ijmedinf.2025.105889.","journal-title":"Int J Med Inform"},{"key":"5403_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2024.117317","volume":"190","author":"CS Ho","year":"2025","unstructured":"Ho CS, Fan TY, Kuo CF, et al. HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs. Bone. 2025;190:117317. https:\/\/doi.org\/10.1016\/j.bone.2024.117317.","journal-title":"Bone"},{"issue":"7","key":"5403_CR79","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1097\/brs.0000000000005595","volume":"51","author":"H Wang","year":"1976","unstructured":"Wang H, Liang S, Huang K, et al. Development and validation of an interpretable machine learning model for predicting early fusion after anterior cervical discectomy and fusion. Spine (Phila Pa 1976). 1976;51(7):468\u201377. https:\/\/doi.org\/10.1097\/brs.0000000000005595.","journal-title":"Spine (Phila Pa 1976)"},{"key":"5403_CR80","unstructured":"American College of Radiology. ACR-SPR-SSR practice parameter for the performance of quantitative computed tomography (QCT) bone mineral density [Internet]. Reston, VA: American College of Radiology; 2023. Available from: https:\/\/gravitas.acr.org\/PPTS\/GetDocumentView?docId=11. Accessed 2 Oct 2026."},{"issue":"1","key":"5403_CR81","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/s00223-025-01445-y","volume":"116","author":"H Zheng","year":"2025","unstructured":"Zheng H, Li X, Cai H, Xiao W, Zhou Z, Zheng L. Evaluating bone mass with opportunistic chest CT: T8 as the optimal vertebral level. Calcif Tissue Int. 2025;116(1):142. https:\/\/doi.org\/10.1007\/s00223-025-01445-y.","journal-title":"Calcif Tissue Int"}],"container-title":["Skeletal Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00256-026-05403-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00256-026-05403-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00256-026-05403-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T23:48:01Z","timestamp":1792108081000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00256-026-05403-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,15]]},"references-count":81,"alternative-id":["5403"],"URL":"https:\/\/doi.org\/10.1007\/s00256-026-05403-z","relation":{},"ISSN":["0364-2348","1432-2161"],"issn-type":[{"value":"0364-2348","type":"print"},{"value":"1432-2161","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,15]]},"assertion":[{"value":"23 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}