{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:17:08Z","timestamp":1793276228354,"version":"4.5.0"},"reference-count":102,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,12]],"date-time":"2024-05-12T00:00:00Z","timestamp":1715472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MOH\/NMRC","award":["CIAINV23jan-0001"],"award-info":[{"award-number":["CIAINV23jan-0001"]}]},{"name":"MOH\/NMRC","award":["MOH-001405"],"award-info":[{"award-number":["MOH-001405"]}]},{"name":"Singapore Ministry of Health National Medical Research Council","award":["CIAINV23jan-0001"],"award-info":[{"award-number":["CIAINV23jan-0001"]}]},{"name":"Singapore Ministry of Health National Medical Research Council","award":["MOH-001405"],"award-info":[{"award-number":["MOH-001405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs. This review investigates the application of AI classification of osteoporosis in radiographs. A comprehensive exploration of electronic repositories (ClinicalTrials.gov, Web of Science, PubMed, MEDLINE) was carried out in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement (PRISMA). A collection of 31 articles was extracted from these repositories and their significant outcomes were consolidated and outlined. This encompassed insights into anatomical regions, the specific machine learning methods employed, the effectiveness in predicting BMD, and categorizing osteoporosis. Through analyzing the respective studies, we evaluated the effectiveness and limitations of AI osteoporosis classification in radiographs. The pooled reported accuracy, sensitivity, and specificity of osteoporosis classification ranges from 66.1% to 97.9%, 67.4% to 100.0%, and 60.0% to 97.5% respectively. This review underscores the potential of AI osteoporosis classification and offers valuable insights for future research endeavors, which should focus on addressing the challenges in technical and clinical integration to facilitate practical implementation of this technology.<\/jats:p>","DOI":"10.3390\/bioengineering11050484","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T08:03:08Z","timestamp":1715673788000},"page":"484","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs\u2014A Systematic Review"],"prefix":"10.3390","volume":"11","author":[{"given":"Ren Wei","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wilson","family":"Ong","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9631-4121","authenticated-orcid":false,"given":"Andrew","family":"Makmur","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore"},{"name":"Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4538-5031","authenticated-orcid":false,"given":"Naresh","family":"Kumar","sequence":"additional","affiliation":[{"name":"University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1240-5908","authenticated-orcid":false,"given":"Xi Zhen","family":"Low","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore"},{"name":"Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Shuliang","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore"},{"name":"Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tan Yi","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore"},{"name":"Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominic Fong Kuan","family":"Ting","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore"},{"name":"Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiong Hao","family":"Tan","sequence":"additional","affiliation":[{"name":"University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0082-9033","authenticated-orcid":false,"given":"James Thomas Patrick Decourcy","family":"Hallinan","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore"},{"name":"Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,12]]},"reference":[{"key":"ref_1","first-page":"1017","article-title":"Osteoporosis: Pathophysiology and therapeutic options","volume":"19","author":"Dovjak","year":"2020","journal-title":"EXCLI J."},{"key":"ref_2","first-page":"e43031","article-title":"Osteoporosis: A Narrative Review","volume":"15","year":"2023","journal-title":"Cureus"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1001\/jama.2018.7498","article-title":"Screening for Osteoporosis to Prevent Fractures","volume":"319","author":"Curry","year":"2018","journal-title":"JAMA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1136\/bjsports-2021-104634","article-title":"Strong, steady and straight: UK consensus statement on physical activity and exercise for osteoporosis","volume":"56","author":"Skelton","year":"2022","journal-title":"Br. J. Sports Med."},{"key":"ref_5","unstructured":"Keen, M.U., and Reddivari, A.K.R. (2024). Osteoporosis in Females, StatPearls."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101766","DOI":"10.1016\/j.berh.2022.101766","article-title":"Male osteoporosis-what are the causes, diagnostic challenges, and management","volume":"36","author":"Clarke","year":"2022","journal-title":"Best Pract. Res. Clin. Rheumatol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s001980070064","article-title":"Long-Term Risk of Osteoporotic Fracture in Malm\u00f6","volume":"11","author":"Kanis","year":"2000","journal-title":"Osteoporos. Int."},{"key":"ref_8","unstructured":"Kanis, J. (2007). WHO Scientific Group Technical Report. 2007, WHO Collaborating Centre for Metabolic Bone Diseases, University of Sheffield."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1046\/j.1532-5415.2002.50455.x","article-title":"Mortality, Disability, and Nursing Home Use for Persons with and without Hip Fracture: A Population-Based Study","volume":"50","author":"Leibson","year":"2002","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_10","first-page":"105","article-title":"The epidemiology of osteoporosis","volume":"133","author":"Clynes","year":"2020","journal-title":"Br. Med. Bull."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.21037\/qims-2020-21","article-title":"Radiographic osteoporotic vertebral fractures in elderly men: A brief review focusing on differences between the sexes","volume":"10","author":"Lentle","year":"2020","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_12","unstructured":"Office of the Surgeon General (US) (2004). Bone Health and Osteoporosis: A Report of the Surgeon General, Office of the Surgeon General (US)."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1359\/jbmr.061113","article-title":"Incidence and Economic Burden of Osteoporosis-Related Fractures in the United States, 2005\u20132025","volume":"22","author":"Burge","year":"2007","journal-title":"J. Bone Miner. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s11657-013-0136-1","article-title":"Osteoporosis in the European Union: Medical management, epidemiology and economic burden","volume":"8","author":"Hernlund","year":"2013","journal-title":"Arch. Osteoporos."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s00198-009-1007-x","article-title":"Population screening for osteoporosis risk: A randomised control trial of medication use and fracture risk","volume":"21","author":"Barr","year":"2010","journal-title":"Osteoporos. Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1210\/jc.2015-3156","article-title":"Underdiagnoses and Undertreatment of Osteoporosis: The Battle to Be Won","volume":"101","author":"Miller","year":"2016","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"190","DOI":"10.11622\/smedj.2021039","article-title":"Assessing the knowledge, attitude and practice of osteoporosis among Singaporean women aged 65 years and above at two SingHealth polyclinics","volume":"62","author":"Lulla","year":"2021","journal-title":"Singap. Med. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s11657-023-01229-7","article-title":"Treatment rates and healthcare costs of patients with fragility fracture by site of care: A real-world data analysis","volume":"18","author":"Singer","year":"2023","journal-title":"Arch. Osteoporos."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1001\/jama.288.15.1889","article-title":"Clinical Use of Bone Densitometry","volume":"288","author":"Cummings","year":"2002","journal-title":"JAMA"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.jocd.2011.05.017","article-title":"Official Positions for FRAX\u00ae Bone Mineral Density and FRAX\u00ae Simplification","volume":"14","author":"Lewiecki","year":"2011","journal-title":"J. Clin. Densitom."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s00198-004-1811-2","article-title":"Requirements for DXA for the management of osteoporosis in Europe","volume":"16","author":"Kanis","year":"2005","journal-title":"Osteoporos. Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.jocd.2017.06.017","article-title":"Quantitative Computed Tomography\u2014Current Status and New Developments","volume":"20","author":"Engelke","year":"2017","journal-title":"J. Clin. Densitom."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s00198-016-3722-4","article-title":"Prediction of hip osteoporosis by DXA using a novel pulse-echo ultrasound device","volume":"28","author":"Schousboe","year":"2017","journal-title":"Osteoporos. Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1007\/s00256-018-2876-x","article-title":"Peripheral DXA measurement around ankle joint to diagnose osteoporosis as assessed by central DXA measurement","volume":"47","author":"Sung","year":"2018","journal-title":"Skelet. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/S0009-9260(60)80012-8","article-title":"The radiological diagnosis of osteoporosis: A new approach","volume":"11","author":"Barnett","year":"1960","journal-title":"Clin. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1016\/S0140-6736(69)92482-9","article-title":"Method for Measuring Quantity of Bone","volume":"294","author":"Millard","year":"1969","journal-title":"Lancet"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1093\/ajcn\/10.1.59","article-title":"An Annotated Bibliography on Bone Densitometry","volume":"10","author":"Garn","year":"1962","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"483","DOI":"10.3109\/00016924909138230","article-title":"A method for the quantitative roentgenological determination of the amount of calcium salts in bone tissue","volume":"31","author":"Engstrom","year":"1949","journal-title":"Acta Radiol."},{"key":"ref_29","first-page":"281","article-title":"The measurement of bone opacity","volume":"6","author":"Bywaters","year":"1948","journal-title":"Clin. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"14193","DOI":"10.1007\/s11042-022-13911-y","article-title":"Osteoporosis diagnosis in knee X-rays by transfer learning based on convolution neural network","volume":"82","author":"Wani","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.jhsa.2019.11.019","article-title":"Convolutional Neural Network for Second Metacarpal Radiographic Osteoporosis Screening","volume":"45","author":"Tecle","year":"2020","journal-title":"J. Hand Surg."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compbiomed.2017.10.011","article-title":"Classification of the trabecular bone structure of osteoporotic patients using machine vision","volume":"91","author":"Singh","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cui, J., Liu, C.L., Jennane, R., Ai, S., Dai, K., and Tsai, T.-Y. (2023). A highly generalized classifier for osteoporosis radiography based on multiscale fractal, lacunarity, and entropy distributions. Front. Bioeng. Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1054991"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1002\/jbmr.4477","article-title":"Opportunistic Osteoporosis Screening Using Chest Radiographs with Deep Learning: Development and External Validation with a Cohort Dataset","volume":"37","author":"Jang","year":"2022","journal-title":"J. Bone Miner. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sato, Y., Yamamoto, N., Inagaki, N., Iesaki, Y., Asamoto, T., and Suzuki, T. (2022). Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study. Biomedicines, 10.","DOI":"10.3390\/biomedicines10092323"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","article-title":"A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis","volume":"1","author":"Liu","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_37","first-page":"19","article-title":"A Path for Translation of Machine Learning Products into Healthcare Delivery","volume":"10","author":"Sendak","year":"2020","journal-title":"EMJ Innov."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e231236","DOI":"10.1148\/radiol.231236","article-title":"Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion","volume":"308","author":"Laustrup","year":"2023","journal-title":"Radiology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e703","DOI":"10.1016\/S2589-7500(23)00153-X","article-title":"Artificial intelligence for breast cancer detection in screening mammography in Sweden: A prospective, population-based, paired-reader, non-inferiority study","volume":"5","author":"Dembrower","year":"2023","journal-title":"Lancet. Digit. Health"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1148\/radiol.2021204289","article-title":"Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI","volume":"300","author":"Hallinan","year":"2021","journal-title":"Radiology"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1148\/radiol.220076","article-title":"Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI","volume":"305","author":"Lim","year":"2022","journal-title":"Radiology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e005292","DOI":"10.1136\/jitc-2022-005292","article-title":"Artificial intelligence and radiomics: Fundamentals, applications, and challenges in immunotherapy","volume":"10","author":"Dercle","year":"2022","journal-title":"J. ImmunoTher. Cancer"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"485","DOI":"10.5152\/dir.2019.19321","article-title":"Radiomics with artificial intelligence: A practical guide for beginners","volume":"25","author":"Kocak","year":"2019","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine Learning for Medical Imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"RadioGraphics"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13244-019-0832-5","article-title":"Deep learning workflow in radiology: A primer","volume":"11","author":"Montagnon","year":"2020","journal-title":"Insights Into Imaging"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1148\/radiol.2020192224","article-title":"Preparing Medical Imaging Data for Machine Learning","volume":"295","author":"Willemink","year":"2020","journal-title":"Radiology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1593\/tlo.13799","article-title":"Multisite Image Data Collection and Management Using the RSNA Image Sharing Network","volume":"7","author":"Erickson","year":"2014","journal-title":"Transl. Oncol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","article-title":"Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges","volume":"32","author":"Hesamian","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6662911","DOI":"10.1155\/2023\/6662911","article-title":"Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection","volume":"2023","author":"Widyaningrum","year":"2023","journal-title":"Int. J. Dent."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101410","DOI":"10.1016\/j.jocd.2023.101410","article-title":"Evaluation of AI-based spine segmentation (SpS) for the analysis of lumbar spine dual energy X-ray absorptiometry scans","volume":"26","author":"Doctorant","year":"2023","journal-title":"J. Clin. Densitom."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"450","DOI":"10.5152\/dir.2022.211297","article-title":"Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: Focus on radiomics","volume":"28","author":"Kocak","year":"2022","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12065-020-00540-3","article-title":"Convolutional neural networks in medical image understanding: A survey","volume":"15","author":"Sarvamangala","year":"2022","journal-title":"Evol. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/s40537-019-0276-2","article-title":"Deep convolutional neural network based medical image classification for disease diagnosis","volume":"6","author":"Yadav","year":"2019","journal-title":"J. Big Data"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11657-021-00985-8","article-title":"Application of deep learning neural network in predicting bone mineral density from plain X-ray radiography","volume":"16","author":"Ho","year":"2021","journal-title":"Arch Osteoporos"},{"key":"ref_57","first-page":"953","article-title":"Diagnosis of Osteoporosis using modified U-net architecture with attention unit in DEXA and X-ray images","volume":"28","author":"Tamilselvi","year":"2020","journal-title":"J. X-ray Sci. Technol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5472","DOI":"10.1038\/s41467-021-25779-x","article-title":"Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning","volume":"12","author":"Hsieh","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"6088","DOI":"10.1038\/s41598-022-10150-x","article-title":"Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates","volume":"12","author":"Sukegawa","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yamamoto, N., Sukegawa, S., Kitamura, A., Goto, R., Noda, T., and Nakano, K. (2020). Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates. Biomolecules, 10.","DOI":"10.3390\/biom10111534"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lee, K.S., Jung, S.K., Ryu, J.J., Shin, S.W., and Choi, J. (2020). Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs. J. Clin. Med., 9.","DOI":"10.3390\/jcm9020392"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"115561","DOI":"10.1016\/j.bone.2020.115561","article-title":"Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study","volume":"140","author":"Zhang","year":"2020","journal-title":"Bone"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s00198-017-4328-1","article-title":"Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population","volume":"29","author":"Areeckal","year":"2018","journal-title":"Osteoporos. Int."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.oooo.2014.11.009","article-title":"Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women","volume":"119","author":"Kavitha","year":"2015","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"20160076","DOI":"10.1259\/dmfr.20160076","article-title":"Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches","volume":"45","author":"Kavitha","year":"2016","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chu, P., Bo, C., Liang, X., Yang, J., Megalooikonomou, V., and Yang, F. (2018, January 18\u201321). Using Octuplet Siamese Network For Osteoporosis Analysis On Dental Panoramic Radiographs. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512755"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"20170006","DOI":"10.1259\/dmfr.20170006","article-title":"Strut analysis for osteoporosis detection model using dental panoramic radiography","volume":"46","author":"Hwang","year":"2017","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"20170344","DOI":"10.1259\/dmfr.20170344","article-title":"Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: A preliminary study","volume":"48","author":"Lee","year":"2019","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1109\/TMI.2017.2708988","article-title":"Anisotropic Discrete Dual-Tree Wavelet Transform for Improved Classification of Trabecular Bone","volume":"36","author":"Oulhaj","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"101885","DOI":"10.1016\/j.artmed.2020.101885","article-title":"Integrative blockwise sparse analysis for tissue characterization and classification","volume":"107","author":"Zheng","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_71","unstructured":"Jennane, R. (2017). 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Fez, Morocco, 22\u201324 May 2017, IEEE Xplore."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Bhattacharya, S., Nair, D., Bhan, A., and Goyal, A. (2019, January 7\u20138). Computer Based Automatic Detection and Classification of Osteoporosis in Bone Radiographs. Proceedings of the 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN.2019.8711616"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"19997","DOI":"10.1038\/s41598-021-99549-6","article-title":"Prediction of osteoporosis from simple hip radiography using deep learning algorithm","volume":"11","author":"Jang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Nguyen, T.P., Chae, D.-S., Park, S.-J., and Yoon, J. (2021). A novel approach for evaluating bone mineral density of hips based on Sobel gradient-based map of radiographs utilizing convolutional neural network. Comput. Biol. Med., 132.","DOI":"10.1016\/j.compbiomed.2021.104298"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Singh, Y., Atulkar, V., Ren, J., Yang, J., Fan, H., Latecki, L.J., and Ling, H. (2021, January 1\u20135). Osteoporosis Prescreening and Bone Mineral Density Prediction using Dental Panoramic Radiographs. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico City, Mexico.","DOI":"10.1109\/EMBC46164.2021.9630183"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1002\/jbmr.4814","article-title":"Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-ray Radiography","volume":"38","author":"Hong","year":"2023","journal-title":"J. Bone Miner. Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"20220135","DOI":"10.1259\/dmfr.20220135","article-title":"Osteoporosis screening support system from panoramic radiographs using deep learning by convolutional neural network","volume":"51","author":"Nakamoto","year":"2022","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s00256-019-03342-6","article-title":"The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population","volume":"49","author":"Lee","year":"2020","journal-title":"Skelet. Radiol."},{"key":"ref_79","first-page":"911","article-title":"Opportunistic Screening for Osteoporosis Using Hand Radiographs: A Preliminary Study","volume":"302","author":"Mohammadi","year":"2023","journal-title":"Stud. Health Technol. Inf."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"971877","DOI":"10.3389\/fendo.2022.971877","article-title":"Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population","volume":"13","author":"Mao","year":"2022","journal-title":"Front. Endocrinol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"832","DOI":"10.7326\/0003-4819-144-11-200606060-00009","article-title":"Meta-Analysis: Accuracy of Quantitative Ultrasound for Identifying Patients with Osteoporosis","volume":"144","author":"Nayak","year":"2006","journal-title":"Ann. Intern. Med."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s00198-011-1817-5","article-title":"Quantitative ultrasound of the heel and fracture risk assessment: An updated meta-analysis","volume":"23","author":"Moayyeri","year":"2012","journal-title":"Osteoporos. Int."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-022-00592-y","article-title":"Machine learning for medical imaging: Methodological failures and recommendations for the future","volume":"5","author":"Varoquaux","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1111\/1754-9485.13261","article-title":"A review of medical image data augmentation techniques for deep learning applications","volume":"65","author":"Chlap","year":"2021","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Torrey, L., and Shavlik, J. (2010). Handbook of Research on Machine Learning Applications and Trends, IGI Global.","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4581","DOI":"10.1038\/s41467-022-32186-3","article-title":"Addressing fairness in artificial intelligence for medical imaging","volume":"13","author":"Echeveste","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.jacr.2023.06.025","article-title":"\u201cShortcuts\u201d Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation","volume":"20","author":"Banerjee","year":"2023","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Martins, M.V., Baptista, L., Lu\u00eds, H., Assun\u00e7\u00e3o, V., Ara\u00fajo, M.-R., and Realinho, V. (2023). Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress. Computation, 11.","DOI":"10.3390\/computation11060115"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"12865","DOI":"10.1038\/s41598-023-40104-w","article-title":"Osteoporosis screening using machine learning and electromagnetic waves","volume":"13","author":"Albuquerque","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Socha, M., Pra\u017cuch, W., Suwalska, A., Foszner, P., Tobiasz, J., Jaroszewicz, J., Gruszczynska, K., Sliwinska, M., Nowak, M., and Gizycka, B. (2023). Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis. Comput. Methods Programs Biomed., 240.","DOI":"10.1016\/j.cmpb.2023.107684"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s00330-020-07148-2","article-title":"Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow","volume":"31","author":"Kotter","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"3576","DOI":"10.1007\/s00330-020-06672-5","article-title":"Integrating artificial intelligence into the clinical practice of radiology: Challenges and recommendations","volume":"30","author":"Recht","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1148\/radiol.212151","article-title":"Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?","volume":"305","author":"Daye","year":"2022","journal-title":"Radiology"},{"key":"ref_94","unstructured":"Pesapane, F., Suter, M.B., Codari, M., Patella, F., Volont\u00e9, C., and Sardanelli, F. (2020). Precision Medicine for Investigators, Practitioners and Providers, Elsevier."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"051804","DOI":"10.1117\/1.JMI.10.5.051804","article-title":"Regulatory considerations for medical imaging AI\/ML devices in the United States: Concepts and challenges","volume":"10","author":"Petrick","year":"2023","journal-title":"J. Med. Imaging"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_97","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2014). Striving for Simplicity: The All Convolutional Net. arxiv."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Rahim, F., Zaki Zadeh, A., Javanmardi, P., Emmanuel Komolafe, T., Khalafi, M., Arjomandi, A., Ghofrani, H.A., and Shirbandi, K. (2023). Machine learning algorithms for diagnosis of hip bone osteoporosis: A systematic review and meta-analysis study. BioMed. Eng. OnLine, 22.","DOI":"10.1186\/s12938-023-01132-9"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"e220042","DOI":"10.1148\/ryai.220042","article-title":"Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool","volume":"4","author":"Pickhardt","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.21037\/qims-21-587","article-title":"Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence","volume":"12","author":"Zhao","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s00198-020-05737-x","article-title":"Evolution in fracture risk assessment: Artificial versus augmented intelligence","volume":"32","author":"Hans","year":"2021","journal-title":"Osteoporos. Int."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1002\/jbmr.4805","article-title":"Hip Fracture Risk Assessment in Elderly and Diabetic Patients: Combining Autonomous Finite Element Analysis and Machine Learning","volume":"38","author":"Yosibash","year":"2023","journal-title":"J. Bone Miner. Res."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/5\/484\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:41:10Z","timestamp":1760107270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/5\/484"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,12]]},"references-count":102,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["bioengineering11050484"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11050484","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,12]]}}}