{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:25:07Z","timestamp":1778084707369,"version":"3.51.4"},"reference-count":41,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Decision Technologies"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Osteoporosis is an ailment associated with the bones, in which the bone resorption takes place at a much faster pace as compared to the formation of bones, eventually leading to the deterioration of bone mineral density (BMD). Ultimately, it adversely affects the strength of bones. To determine different diseases, deep learning is used in almost every sector of healthcare. In the context of Osteoporosis, there are numerous machine learning technologies that have been utilized for early detection of the disease. Certainly, these techniques provided great accuracy, but their scope of study was limited exclusively to individual factors. This paper proposes a model which studies multiple aspects leading to the early prognosis of disease, thus increasing the reliability. The aspects are Bone Density Measure, the X-rays of affected bone, lifestyle of the patient which may include medical history if any, fracture status and the specific bone. The dataset used for the research contains 2000 X-rays in total and 500\u00a0BMD reports of 500 distinct patients. in Logistic regression is used for the BMD based classification, where the accuracy achieved is 98.66%, with a recall of 97%, precision of 100% and f1-score of 98% for osteoporotic category. The VGG16 model used for the classification based on image dataset achieves the accuracy of 97.19% which is acceptable comparative to existing methods.<\/jats:p>","DOI":"10.3233\/idt-240227","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T11:22:59Z","timestamp":1731669779000},"page":"395-413","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Osteoporosis classification using VGG-16 and logistic regression: A\u00a0radiograph and BMD data approach"],"prefix":"10.1177","volume":"19","author":[{"given":"Dipmala","family":"Salunke","sequence":"first","affiliation":[{"name":"JSPM\u2019s Rajarshi Shahu College of Engineering, Tathawade, Pune, India"}]},{"given":"Gayatri","family":"Joshi","sequence":"additional","affiliation":[{"name":"JSPM\u2019s Rajarshi Shahu College of Engineering, Tathawade, Pune, India"}]},{"given":"Sneha","family":"Inamdar","sequence":"additional","affiliation":[{"name":"JSPM\u2019s Rajarshi Shahu College of Engineering, Tathawade, Pune, India"}]},{"given":"Manasi","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"JSPM\u2019s Rajarshi Shahu College of Engineering, Tathawade, Pune, India"}]}],"member":"179","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07655-2"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08323-9"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-99549-6"},{"issue":"2","key":"e_1_3_1_5_2","first-page":"691","article-title":"Machine learning for opportunistic screening for osteoporosis from CT scans of the wrist and forearm","volume":"12","author":"De la Garza Ramos C","year":"2022","unstructured":"De la Garza Ramos C, Sebro R. Machine learning for opportunistic screening for osteoporosis from CT scans of the wrist and forearm. Diagnostics (Basel). 2022 11; 12(2): 691.","journal-title":"Diagnostics (Basel)"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.14336\/AD.2021.1206"},{"issue":"1","key":"e_1_3_1_7_2","first-page":"173","article-title":"Characteristics of long-term femoral neck bone loss in postmenopausal women: a 25-year follow-up","volume":"37","author":"Moilanen A","year":"2022","unstructured":"Moilanen A, Kopra J, Kr\u00f6ger H, Sund R, Rikkonen T, Sirola J. Characteristics of long-term femoral neck bone loss in postmenopausal women: a 25-year follow-up. Journal of Bone and Mineral Research. 2022; 37(1): 173-8.","journal-title":"Journal of Bone and Mineral Research"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.2147\/CIA.S405317"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/ma15228048"},{"key":"e_1_3_1_10_2","first-page":"14","article-title":"Osteoporosis in 10 years: a glimpse into the future of osteoporosis","author":"Adami G","year":"2022","unstructured":"Adami G, Fassio A, Gatti D, Viapiana O, Benini C, Danila MI, et\u00a0al. Osteoporosis in 10 years: a glimpse into the future of osteoporosis. Ther Adv Musculoskelet Dis. 2022; 14.","journal-title":"Ther Adv Musculoskelet Dis"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.berh.2022.101754"},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Genisa M Abdullah JY Yusoff BM Arief EM Hermana M Utomo CP. Adopting signal processing technique for osteoporosis detection based on CT scan image. Applied Sciences. 2023; 13(8).","DOI":"10.3390\/app13085094"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.22271\/ortho.2021.v7.i3b.2736"},{"issue":"3","key":"e_1_3_1_14_2","first-page":"2455","article-title":"A review on osteoporosis","volume":"10","author":"Rose D","year":"2022","unstructured":"Rose D, Prakasam CKC, Preethi T. A review on osteoporosis. Int J All Res Educ Sci Methods. 2022; 10(3): 2455-6211.","journal-title":"Int J All Res Educ Sci Methods"},{"issue":"2","key":"e_1_3_1_15_2","article-title":"Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: a retrospective single center preliminary study","volume":"16","author":"Lim HK","year":"2021","unstructured":"Lim HK, Ha HI, Park SY, Han J. Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: a retrospective single center preliminary study. PLoS One. 2021 Mar 4; 16(2): e0247330.","journal-title":"PLoS One"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18147635"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.48084\/etasr.3637"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/6662911"},{"issue":"2","key":"e_1_3_1_19_2","first-page":"1","article-title":"Osteoporosis: detection using dental radiography","volume":"2","author":"Sharma S","year":"2020","unstructured":"Sharma S, Agarwal A, Singh CS. Osteoporosis: detection using dental radiography. J Dent Oral Sci. 2020; 2(2): 1-7.","journal-title":"J Dent Oral Sci"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-10150-x"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-021-05852-3"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/biom10111534"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-25779-x"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13018-021-02351-3"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1177\/1759720X211024029"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1002\/jbmr.4575"},{"key":"e_1_3_1_27_2","first-page":"180109543","article-title":"Expert systems for early prediction of osteoporosis using multi-model algorithms","author":"Prakash UM","year":"2021","unstructured":"Prakash UM, Kottursamy K, Cengiz K, K\u00f6se U, Bui H. Expert systems for early prediction of osteoporosis using multi-model algorithms. Measurement. 2021; 180109543.","journal-title":"Measurement"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22218189"},{"key":"e_1_3_1_29_2","unstructured":"https:\/\/www.arthritiswa.org.au\/osteoporosis\/."},{"key":"e_1_3_1_30_2","unstructured":"https:\/\/www.everydayhealth.com\/osteoporosis\/even-during-drug-holiday-osteoporosis-patients-should-monitored\/."},{"key":"e_1_3_1_31_2","unstructured":"https:\/\/lowcountryfamilydentistry.com\/osteoporosis-oral-health\/."},{"key":"e_1_3_1_32_2","unstructured":"https:\/\/www.kaggle.com\/datasets\/amarsharma768\/bmd-data."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.14445\/22315381\/IJETT-V71I2P208"},{"issue":"91","key":"e_1_3_1_34_2","first-page":"827","article-title":"Customized convolutional neural network to detect dental caries from radiovisiography (RVG) images","volume":"9","author":"Salunke D","year":"2022","unstructured":"Salunke D, Mane D, Joshi R, Peddi P. Customized convolutional neural network to detect dental caries from radiovisiography (RVG) images. Int J Adv Technol Eng Explor. 2022; 9(91): 827-38.","journal-title":"Int J Adv Technol Eng Explor"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.18280\/ts.390622"},{"key":"e_1_3_1_36_2","doi-asserted-by":"crossref","unstructured":"Salunke D Joshi R Peddi P Mane D. Deep learning techniques for dental image diagnostics: A survey. In: Proceedings of the IEEE International Conference on Artificial Intelligence in Smart Systems. 2022; 244-57.","DOI":"10.1109\/ICAISS55157.2022.10010576"},{"issue":"2","key":"e_1_3_1_37_2","first-page":"518","article-title":"The significance of image augmentation in deep learning: A review","volume":"11","author":"Salunke D","year":"2022","unstructured":"Salunke D, Peddi P, Joshi R. The significance of image augmentation in deep learning: A review. Int J Adv Res Comput Commun Eng. 2022; 11(2): 518-23.","journal-title":"Int J Adv Res Comput Commun Eng"},{"key":"e_1_3_1_38_2","unstructured":"https:\/\/www.researchgate.net\/figure\/VGG16-architecture-for-spondylolisthesis-diagnosis_fig1_359933899."},{"issue":"8","key":"e_1_3_1_39_2","article-title":"Evaluation of parameter fine-tuning with transfer learning for osteoporosis classification in knee radiograph","volume":"13","author":"Abubakar U","year":"2022","unstructured":"Abubakar U, Boukar M, Adeshina S. Evaluation of parameter fine-tuning with transfer learning for osteoporosis classification in knee radiograph. Int J Adv Comput Sci Appl. 2022; 13(8).","journal-title":"Int J Adv Comput Sci Appl"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.3991\/ijoe.v19i08.39235"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20220135"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm9020392"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDT-240227","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/IDT-240227","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDT-240227","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:21:03Z","timestamp":1777454463000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IDT-240227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.3233\/IDT-240227"],"URL":"https:\/\/doi.org\/10.3233\/idt-240227","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,13]]}}}