{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T07:53:38Z","timestamp":1791186818826,"version":"4.1.0"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.cmpb.2026.109655","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T23:10:26Z","timestamp":1790809826000},"page":"109655","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Fragility Fracture Risk Prediction through the integration of Artificial Intelligence and Radiofrequency Echographic Multi Spectrometry (REMS) Technology"],"prefix":"10.1016","volume":"288","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2687-661X","authenticated-orcid":false,"given":"Giusy","family":"Peluso","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9537-8334","authenticated-orcid":false,"given":"Francesco","family":"Conversano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3692-0688","authenticated-orcid":false,"given":"Mauro","family":"Bellone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2675-9237","authenticated-orcid":false,"given":"Fiorella Anna","family":"Lombardi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paola","family":"Pisani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6408-1335","authenticated-orcid":false,"given":"Italo","family":"Epicoco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1118-7109","authenticated-orcid":false,"given":"Massimo","family":"Cafaro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1940-4817","authenticated-orcid":false,"given":"Sergio","family":"Casciaro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2026.109655_b1","doi-asserted-by":"crossref","first-page":"54310","DOI":"10.1109\/ACCESS.2021.3071301","article-title":"Deep learning in medical ultrasound image analysis: A review","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.cmpb.2026.109655_b2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"issue":"7","key":"10.1016\/j.cmpb.2026.109655_b3","doi-asserted-by":"crossref","DOI":"10.3390\/su15075930","article-title":"A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope","volume":"15","author":"Salehi","year":"2023","journal-title":"Sustainability"},{"issue":"3","key":"10.1016\/j.cmpb.2026.109655_b4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.3390\/info16030195","article-title":"Deep convolutional neural networks in medical image analysis: A review","volume":"16","author":"Mienye","year":"2025","journal-title":"Information"},{"issue":"9","key":"10.1016\/j.cmpb.2026.109655_b5","doi-asserted-by":"crossref","first-page":"2073","DOI":"10.3390\/cells9092073","article-title":"Osteoblast-osteoclast communication and bone homeostasis","volume":"9","author":"Kim","year":"2020","journal-title":"Cells"},{"key":"10.1016\/j.cmpb.2026.109655_b6","article-title":"An overview and management of osteoporosis","volume":"Mar;4(1):46-56","author":"S\u00f6zen","year":"2017","journal-title":"Eur. J. Rheumatol."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109655_b7","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1146\/annurev.bioeng.3.1.307","article-title":"Biomechanics of trabecular bone","volume":"3","author":"Keaveny","year":"2001","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2026.109655_b8","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.ijotn.2017.04.004","article-title":"Reprint of: The impact of fragility fracture and approaches to osteoporosis risk assessment worldwide","volume":"26","author":"Curtis","year":"2017","journal-title":"Int. J. Orthop. Trauma Nurs."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109655_b9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-60173-5","article-title":"Concept of a radiofrequency device for osteopenia\/osteoporosis screening","volume":"10","author":"Makarov","year":"2020","journal-title":"Sci. Rep."},{"issue":"11","key":"10.1016\/j.cmpb.2026.109655_b10","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1002\/jbmr.1968","article-title":"Compound risk of high mortality following osteoporotic fracture and refracture in elderly women and men","volume":"28","author":"Bliuc","year":"2013","journal-title":"J. Bone Miner. Res."},{"key":"10.1016\/j.cmpb.2026.109655_b11","series-title":"Bone Quantitative Ultrasound: New Horizons","first-page":"7","article-title":"Quantitative ultrasound (QUS) in the management of osteoporosis and assessment of fracture risk: an update","author":"Hans","year":"2022"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109655_b12","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.irbm.2018.10.006","article-title":"Quantitative ultrasound assessment of cortical bone properties beyond bone mineral density","volume":"40","author":"Grimal","year":"2019","journal-title":"Irbm"},{"issue":"2","key":"10.1016\/j.cmpb.2026.109655_b13","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1007\/s12020-024-03931-z","article-title":"Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal","volume":"86","author":"Luo","year":"2024","journal-title":"Endocrine"},{"key":"10.1016\/j.cmpb.2026.109655_b14","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1007\/s40520-019-01294-4","volume":"31","author":"Diez-Perez","year":"2019","journal-title":"Aging Clin. Exp. Res."},{"key":"10.1016\/j.cmpb.2026.109655_b15","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s40520-024-02784-w","article-title":"Radiofrequency echographic multi spectrometry (REMS) in the diagnosis and management of osteoporosis: state of the art","volume":"36","author":"Fuggle","year":"2024","journal-title":"Aging Clin. Exp. Res."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109655_b16","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.ultrasmedbio.2014.08.017","article-title":"A novel ultrasound methodology for estimating spine mineral density","volume":"41","author":"Conversano","year":"2015","journal-title":"Ultrasound Med. Biol."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109655_b17","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1177\/1759720X14564562","article-title":"Fragility fracture: recent developments in risk assessment","volume":"7","author":"Aspray","year":"2015","journal-title":"Ther. Adv. Musculoskelet. Dis."},{"issue":"11","key":"10.1016\/j.cmpb.2026.109655_b18","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1093\/jbmr\/zjae143","article-title":"Short-term risk of fracture is increased by deficits in cortical and trabecular bone microarchitecture independent of DXA BMD and FRAX: Bone microarchitecture international consortium (BoMIC) prospective cohorts","volume":"39","author":"Sarfati","year":"2024","journal-title":"J. Bone Miner. Res."},{"key":"10.1016\/j.cmpb.2026.109655_b19","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.measurement.2016.01.033","article-title":"Ultrasound fragility score: an innovative approach for the assessment of bone fragility","volume":"101","author":"Greco","year":"2017","journal-title":"Measurement"},{"issue":"4","key":"10.1016\/j.cmpb.2026.109655_b20","first-page":"763","article-title":"Fragility score: a REMS-based indicator for the prediction of incident fragility fractures at 5 years","volume":"35","author":"Pisani","year":"2023","journal-title":"Aging Clin. Exp. Res."},{"key":"10.1016\/j.cmpb.2026.109655_b21","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.bone.2015.05.016","article-title":"Trabecular bone score (TBS) as a new complementary approach for osteoporosis evaluation in clinical practice","volume":"78","author":"Harvey","year":"2015","journal-title":"Bone"},{"issue":"11","key":"10.1016\/j.cmpb.2026.109655_b22","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1007\/s40618-023-02082-8","article-title":"A systematic review on the performance of fracture risk assessment tools: FRAX, DeFRA, FRA-HS","volume":"46","author":"Adami","year":"2023","journal-title":"J. Endocrinol. Invest."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109655_b23","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","article-title":"Domain adaptation for medical image analysis: A survey","volume":"69","author":"Guan","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"10.1016\/j.cmpb.2026.109655_b24","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/72.80230","article-title":"Perceptron-based learning algorithms","volume":"1","author":"Gallant","year":"1990","journal-title":"IEEE Trans. Neural Netw."},{"issue":"7","key":"10.1016\/j.cmpb.2026.109655_b25","first-page":"579","article-title":"Multilayer perceptron and neural networks","volume":"8","author":"Popescu","year":"2009","journal-title":"WSEAS Trans. Circuits Syst."},{"issue":"11","key":"10.1016\/j.cmpb.2026.109655_b26","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"issue":"6","key":"10.1016\/j.cmpb.2026.109655_b27","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"10.1016\/j.cmpb.2026.109655_b28","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.cmpb.2026.109655_b29","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.cmpb.2026.109655_b30","series-title":"2017 IEEE International Conference on Image Processing","first-page":"790","article-title":"Densenet for dense flow","author":"Zhu","year":"2017"},{"key":"10.1016\/j.cmpb.2026.109655_b31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"10.1016\/j.cmpb.2026.109655_b32","series-title":"Foundations of Machine Learning","author":"Mohri","year":"2012"},{"key":"10.1016\/j.cmpb.2026.109655_b33","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.1007\/s00198-014-2799-x","article-title":"The risk of major and any (non-hip) fragility fracture after hip fracture in the United Kingdom: 2000\u20132010","volume":"25","author":"Gibson-Smith","year":"2014","journal-title":"Osteoporos Int."},{"key":"10.1016\/j.cmpb.2026.109655_b34","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s12291-016-0610-9","article-title":"Lead as a risk factor for osteoporosis in post-menopausal women","volume":"32","author":"Manocha","year":"2017","journal-title":"Indian J. Clin. Biochem."},{"issue":"2","key":"10.1016\/j.cmpb.2026.109655_b35","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s00198-018-4686-3","article-title":"Radiofrequency echographic multispectrometry compared with dual X-ray absorptiometry for osteoporosis diagnosis on lumbar spine and femoral neck","volume":"30","author":"Di Paola","year":"2019","journal-title":"Osteoporos Int."},{"issue":"6","key":"10.1016\/j.cmpb.2026.109655_b36","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1016\/j.ultrasmedbio.2016.01.024","article-title":"An advanced quantitative echosound methodology for femoral neck densitometry","volume":"42","author":"Casciaro","year":"2016","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.cmpb.2026.109655_b37","series-title":"2020 27th National and 5th International Iranian Conference on Biomedical Engineering","first-page":"333","article-title":"Addressing imbalance in multi-label classification using weighted cross entropy loss function","author":"Rezaei-Dastjerdehei","year":"2020"},{"key":"10.1016\/j.cmpb.2026.109655_b38","article-title":"Wiley interdisciplinary reviews: Data mining and knowledge discovery","author":"Lepping","year":"2018","journal-title":"Wiley Interdiscip. Rev.: Data Min. Knowl. Discov."},{"key":"10.1016\/j.cmpb.2026.109655_b39","series-title":"2022 IEEE World AI IoT Congress","first-page":"187","article-title":"Lung cancer prediction model using ensemble learning techniques and a systematic review analysis","author":"Mamun","year":"2022"},{"issue":"4","key":"10.1016\/j.cmpb.2026.109655_b40","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1002\/bimj.200410135","article-title":"Estimation of the youden index and its associated cutoff point","volume":"47","author":"Fluss","year":"2005","journal-title":"Biom. J.: J. Math. Methods Biosci."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109655_b41","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1007\/s00198-016-3868-0","article-title":"Imminent risk of fracture after fracture","volume":"28","author":"Johansson","year":"2017","journal-title":"Osteoporos Int."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726004049?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726004049?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T07:19:49Z","timestamp":1791184789000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260726004049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":41,"alternative-id":["S0169260726004049"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109655","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Fragility Fracture Risk Prediction through the integration of Artificial Intelligence and Radiofrequency Echographic Multi Spectrometry (REMS) Technology","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109655","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109655"}}