{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:36:44Z","timestamp":1783021004489,"version":"3.54.6"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001700","name":"The Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan","doi-asserted-by":"crossref","award":["24K15696"],"award-info":[{"award-number":["24K15696"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100018502","name":"Kanazawa University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100018502","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Biomed Eng"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>While the multidirectional optical bone densitometry approach, originally proposed based on simulation, has been previously reported, its experimental validation has not yet been demonstrated. This study addresses this gap by validating the method using bone phantoms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>These phantoms were fabricated with Intralipos, agarose, and bovine cortical bone fragments. By varying the mixing ratio of cortical bone fragments, phantoms with different bone densities were prepared. Near-infrared laser light with a wavelength of 850\u00a0nm was directed onto the phantoms from three directions, and the resulting backward, lateral, and forward light intensity distributions on the phantom surface were recorded. Directional light intensity values were extracted from these distributions and used as features for the simulation-based machine learning model to predict bone density.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Linear regression between the predicted and reference bone densities yielded an r\n                      <jats:sup>2<\/jats:sup>\n                      of 0.85.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings demonstrate the feasibility of the proposed method as a proof-of-concept validation and suggest a possible direction for future development of optical bone density assessment methods.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10439-026-04258-8","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:21:51Z","timestamp":1783016511000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multidirectional Optical Bone Densitometry Using a Simulation-Based Machine Learning Model: Experimental Validation with Bone Phantoms"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1262-3876","authenticated-orcid":false,"given":"Shigeo M.","family":"Tanaka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaito","family":"Yoshikawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"4258_CR1","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1172\/jci110039","volume":"67","author":"BL Riggs","year":"1981","unstructured":"Riggs, B. L., H. W. Wahner, W. L. Dunn, R. B. Mazess, K. P. Offord, and L. J. Melton. Differential changes in bone mineral density of the appendicular and axial skeleton with aging: relationship to spinal osteoporosis. J Clin Investig. 67:328\u2013335, 1981. https:\/\/doi.org\/10.1172\/jci110039.","journal-title":"J Clin Investig."},{"key":"4258_CR2","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1016\/s0140-6736(02)08657-9","volume":"359","author":"SR Cummings","year":"2002","unstructured":"Cummings, S. R., and L. J. Melton. Epidemiology and outcomes of osteoporotic fractures. Lancet. 359:1761\u20131767, 2002. https:\/\/doi.org\/10.1016\/s0140-6736(02)08657-9.","journal-title":"Lancet."},{"key":"4258_CR3","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1007\/s00198-006-0172-4","volume":"17","author":"O Johnell","year":"2006","unstructured":"Johnell, O., and J. A. Kanis. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int. 17:1726\u20131733, 2006. https:\/\/doi.org\/10.1007\/s00198-006-0172-4.","journal-title":"Osteoporos Int."},{"key":"4258_CR4","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1203\/pdr.0b013e318163a286","volume":"63","author":"GI Baroncelli","year":"2008","unstructured":"Baroncelli, G. I. Quantitative ultrasound methods to assess bone mineral status in children: technical characteristics, performance, and clinical application. Pediatr Res. 63:220\u2013228, 2008. https:\/\/doi.org\/10.1203\/pdr.0b013e318163a286.","journal-title":"Pediatr Res."},{"key":"4258_CR5","doi-asserted-by":"publisher","first-page":"742","DOI":"10.3949\/ccjm.73.8.742","volume":"73","author":"EM Lewiecki","year":"2006","unstructured":"Lewiecki, E. M., B. Richmond, and P. D. Miller. Uses and misuses of quantitative ultrasonography in managing osteoporosis. Clevel Clin J Med. 73:742\u2013746, 2006. https:\/\/doi.org\/10.3949\/ccjm.73.8.742.","journal-title":"Clevel Clin J Med."},{"key":"4258_CR6","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1186\/s13018-023-04211-8","volume":"18","author":"W Chanprasertpinyo","year":"2023","unstructured":"Chanprasertpinyo, W., C. Punsawad, R. Khwanchuea, N. Sukkriang, P. Yincharoen, and C. Rerkswattavorn. Comparison between calcaneus quantitative ultrasound and the gold standard DXA in the ability to detect osteoporosis in chronic obstructive pulmonary disease patients. J Orthop Surg Res. 18:778, 2023. https:\/\/doi.org\/10.1186\/s13018-023-04211-8.","journal-title":"J Orthop Surg Res."},{"key":"4258_CR7","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/s10439-023-03387-8","volume":"52","author":"K Miura","year":"2023","unstructured":"Miura, K., S. M. Tanaka, C. Chotipanich, T. Chobpenthai, A. Jantarato, and A. Khantachawana. Osteoporosis prediction using machine-learned optical bone densitometry data. Ann Biomed Eng. 52:396\u2013405, 2023. https:\/\/doi.org\/10.1007\/s10439-023-03387-8.","journal-title":"Ann Biomed Eng."},{"key":"4258_CR8","doi-asserted-by":"publisher","DOI":"10.1117\/1.jbo.27.5.056004","volume":"27","author":"K Miura","year":"2022","unstructured":"Miura, K., A. Khantachawana, and S. M. Tanaka. Optical bone densitometry robust to variation of soft tissue using machine learning techniques: validation by Monte Carlo simulation. J Biomed Opt.27:056004, 2022. https:\/\/doi.org\/10.1117\/1.jbo.27.5.056004.","journal-title":"J Biomed Opt."},{"key":"4258_CR9","doi-asserted-by":"publisher","first-page":"53098","DOI":"10.1109\/access.2022.3174685","volume":"10","author":"C Akbar","year":"2022","unstructured":"Akbar, C., Y. Li, and N. Thoti. Device-simulation-based machine learning technique for the characteristic of line tunnel field-effect transistors. IEEE Access. 10:53098\u201353107, 2022. https:\/\/doi.org\/10.1109\/access.2022.3174685.","journal-title":"IEEE Access."},{"key":"4258_CR10","doi-asserted-by":"publisher","unstructured":"Araghizad AE, Tehranizadeh F, Kilic K, Budak E. Smart tool-related faults monitoring system using process simulation-based machine learning algorithms. J Mach Eng. 2023;23:18\u201332. https:\/\/doi.org\/10.36897\/jme\/174018","DOI":"10.36897\/jme\/174018"},{"key":"4258_CR11","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s11082-021-02837-8","volume":"53","author":"J Piprek","year":"2021","unstructured":"Piprek, J. Simulation-based machine learning for optoelectronic device design: perspectives, problems, and prospects. Opt Quantum Electron. 53:175, 2021. https:\/\/doi.org\/10.1007\/s11082-021-02837-8.","journal-title":"Opt Quantum Electron."},{"key":"4258_CR12","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1007\/s12369-025-01300-y","volume":"17","author":"HM Shehata","year":"2025","unstructured":"Shehata, H. M., Y. Liu, D. Br\u0161\u010di\u0107, and T. Kanda. Simulation-based machine learning for detection of rarely occurring behaviors based on human trajectories. Int J Soc Robot. 17:1485\u20131499, 2025. https:\/\/doi.org\/10.1007\/s12369-025-01300-y.","journal-title":"Int J Soc Robot."},{"key":"4258_CR13","doi-asserted-by":"publisher","first-page":"18406","DOI":"10.1038\/s41598-021-97965-2","volume":"11","author":"S Giannakoulias","year":"2021","unstructured":"Giannakoulias, S., S. R. Shringari, J. J. Ferrie, and E. J. Petersson. Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine. Sci Rep. 11:18406, 2021. https:\/\/doi.org\/10.1038\/s41598-021-97965-2.","journal-title":"Sci Rep."},{"key":"4258_CR14","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1177\/10943420251351125","volume":"39","author":"A Ghorbannia","year":"2025","unstructured":"Ghorbannia, A., C. Tanade, A. Yousef, N. S. Khan, M. Vardhan, J. T. Chi, S. Roychowdhury, A. Das, J. A. Leopold, E. C. Chi, and A. Randles. Simulation-based machine learning for real-time assessment of side-branch hemodynamics in coronary bifurcation lesions. Int J High Perform Comput Appl. 39:678\u2013691, 2025. https:\/\/doi.org\/10.1177\/10943420251351125.","journal-title":"Int J High Perform Comput Appl."},{"key":"4258_CR15","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1039\/d5pm00007f","volume":"2","author":"K Yadava","year":"2025","unstructured":"Yadava, K., and A. Yadav. A molecular simulation-based deep neural network model for deciphering the adsorption of 5-fluorouracil in COFs. RSC Pharm. 2:703\u2013717, 2025. https:\/\/doi.org\/10.1039\/d5pm00007f.","journal-title":"RSC Pharm."},{"key":"4258_CR16","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1515\/acm-2017-0002","volume":"17","author":"A Kozarova","year":"2017","unstructured":"Kozarova, A., M. Kozar, E. Minarikova, and T. Pappova. Identification of the age related skin changes using high-frequency ultrasound. Acta Med Martiniana. 17:15\u201320, 2017. https:\/\/doi.org\/10.1515\/acm-2017-0002.","journal-title":"Acta Med Martiniana."},{"key":"4258_CR17","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/0026-0495(89)90256-4","volume":"38","author":"C Hassager","year":"1989","unstructured":"Hassager, C., J. Borg, and C. Christiansen. Measurement of the subcutaneous fat in the distal forearm by single photon absorptiometry. Metabolism. 38:159\u2013165, 1989. https:\/\/doi.org\/10.1016\/0026-0495(89)90256-4.","journal-title":"Metabolism."},{"key":"4258_CR18","first-page":"155","volume":"15","author":"M Patachia","year":"2013","unstructured":"Patachia, M. A simple continuous-wave diffuse optical tomography system for parametric investigation of different tissue phantoms. J Optoelectron Adv Mater. 15:155\u2013163, 2013.","journal-title":"J Optoelectron Adv Mater."},{"key":"4258_CR19","doi-asserted-by":"publisher","first-page":"4507","DOI":"10.1364\/ao.30.004507","volume":"30","author":"HJ van Staveren","year":"1991","unstructured":"van Staveren, H. J., C. J. M. Moes, J. van Marie, S. A. Prahl, and M. J. C. van Gemert. Light scattering in lntralipid-10% in the wavelength range of 400\u20131100 nm. Appl Opt. 30:4507, 1991. https:\/\/doi.org\/10.1364\/ao.30.004507.","journal-title":"Appl Opt."},{"key":"4258_CR20","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1007\/s00198-009-0993-z","volume":"21","author":"H Chen","year":"2010","unstructured":"Chen, H., X. Zhou, S. Shoumura, S. Emura, and Y. Bunai. Age- and gender-dependent changes in three-dimensional microstructure of cortical and trabecular bone at the human femoral neck. Osteoporos Int. 21:627\u2013636, 2010. https:\/\/doi.org\/10.1007\/s00198-009-0993-z.","journal-title":"Osteoporos Int."},{"key":"4258_CR21","doi-asserted-by":"publisher","first-page":"6508","DOI":"10.1210\/jc.2005-1258","volume":"90","author":"S Boutroy","year":"2005","unstructured":"Boutroy, S., M. L. Bouxsein, F. Munoz, and P. D. Delmas. In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. J Clin Endocrinol Metab. 90:6508\u20136515, 2005. https:\/\/doi.org\/10.1210\/jc.2005-1258.","journal-title":"J Clin Endocrinol Metab."},{"key":"4258_CR22","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1111\/j.1447-073x.2004.00079.x","volume":"79","author":"T Miura","year":"2004","unstructured":"Miura, T., and P. K. Maini. Periodic pattern formation in reaction-diffusion systems: an introduction for numerical simulation. Anat Sci Int. 79:112\u2013123, 2004. https:\/\/doi.org\/10.1111\/j.1447-073x.2004.00079.x.","journal-title":"Anat Sci Int."},{"key":"4258_CR23","doi-asserted-by":"publisher","unstructured":"Ding R, Zhang J, Koushki E, Tayebee R, Ding X. Nonlinear photoacoustic and optical properties of hydroxyapatite and calcium phosphate. Towards a new method for the densitometry of bones. Optik. 2021;226:165922. https:\/\/doi.org\/10.1016\/j.ijleo.2020.165922","DOI":"10.1016\/j.ijleo.2020.165922"},{"key":"4258_CR24","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1088\/0031-9155\/49\/3\/009","volume":"49","author":"N Ugryumova","year":"2004","unstructured":"Ugryumova, N., S. J. Matcher, and D. P. Attenburrow. Measurement of bone mineral density via light scattering. Phys Med Biol. 49:469\u2013483, 2004. https:\/\/doi.org\/10.1088\/0031-9155\/49\/3\/009.","journal-title":"Phys Med Biol."},{"key":"4258_CR25","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1359\/jbmr.1997.12.2.261","volume":"12","author":"A Takeuchi","year":"1997","unstructured":"Takeuchi, A., R. Araki, S. G. Proskurin, Y. Takahashi, Y. Yamada, J. Ishii, S. Katayama, and A. Itabashi. A new method of bone tissue measurement based upon light scattering. J Bone Miner Res. 12:261\u2013266, 1997. https:\/\/doi.org\/10.1359\/jbmr.1997.12.2.261.","journal-title":"J Bone Miner Res."},{"key":"4258_CR26","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1117\/1.1691029","volume":"9","author":"A Pifferi","year":"2004","unstructured":"Pifferi, A., A. Torricelli, P. Taroni, A. Bassi, E. Chikoidze, E. Giambattistelli, and R. Cubeddu. Optical biopsy of bone tissue: a step toward the diagnosis of bone pathologies. J Biomed Opt. 9:474, 2004. https:\/\/doi.org\/10.1117\/1.1691029.","journal-title":"J Biomed Opt."},{"key":"4258_CR27","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/jtehm.2024.3368106","volume":"12","author":"TG Meitei","year":"2024","unstructured":"Meitei, T. G., W.-C. Chang, P.-L. Cheong, Y.-M. Wang, and C.-W. Sun. A study on intelligent optical bone densitometry. IEEE J Transl Eng Health Med. 12:401\u2013412, 2024. https:\/\/doi.org\/10.1109\/jtehm.2024.3368106.","journal-title":"IEEE J Transl Eng Health Med."}],"container-title":["Annals of Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-026-04258-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10439-026-04258-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-026-04258-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:21:52Z","timestamp":1783016512000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10439-026-04258-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"references-count":27,"alternative-id":["4258"],"URL":"https:\/\/doi.org\/10.1007\/s10439-026-04258-8","relation":{},"ISSN":["0090-6964","1573-9686"],"issn-type":[{"value":"0090-6964","type":"print"},{"value":"1573-9686","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]},"assertion":[{"value":"20 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author declares that there are no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"Not applicable","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Registration"}},{"value":"Not applicable.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Number"}}]}}