Zeytinoglu M, Jain RK, Vokes TJ (2017) Vertebral fracture assessment: enhancing the diagnosis, prevention, and treatment of osteoporosis. Bone 104:54–65. https://doi.org/10.1016/j.bone.2017.03.004
Burns JE, Yao J, Summers RM (2020) Artificial intelligence in musculoskeletal imaging: a paradigm shift Review. J Bone Mineral Res 35(1):28–35. https://doi.org/10.1002/jbmr.3849
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
Article CAS PubMed Google Scholar
Geusens P, Hochberg MC, van der Voort DJ et al (2002) Performance of risk indices for identifying low bone density in postmenopausal women. Mayo Clin Proc 77(7):629–637. https://doi.org/10.4065/77.7.629
Koh LK, Sedrine WB, Torralba TP et al (2001) A simple tool to identify asian women at increased risk of osteoporosis. Osteoporos Int 12(8):699–705. https://doi.org/10.1007/s001980170070
Article CAS PubMed Google Scholar
Cadarette SM, Jaglal SB, Murray TM (1999) Validation of the simple calculated osteoporosis risk estimation (SCORE) for patient selection for bone densitometry. Osteoporos Int 10(1):85–90. https://doi.org/10.1007/s001980050199
Article CAS PubMed Google Scholar
Cadarette SM, Jaglal SB, Kreiger N, McIsaac WJ, Darlington GA, Tu JV (2000) Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry. CMAJ 162(9):1289–1294
CAS PubMed PubMed Central Google Scholar
Weinstein L, Ullery B (2000) Identification of at-risk women for osteoporosis screening. Am J Obstet Gynecol 183(3):547–549. https://doi.org/10.1067/mob.2000.106594
Article CAS PubMed Google Scholar
Sedrine WB, Chevallier T, Zegels B et al (2002) Development and assessment of the Osteoporosis Index of Risk (OSIRIS) to facilitate selection of women for bone densitometry. Gynecol Endocrinol 16(3):245–250
Article CAS PubMed Google Scholar
Yen TY, Ho CS, Pei YC et al (2024) Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks. Bone 184:117107. https://doi.org/10.1016/j.bone.2024.117107
Uragami M, Matsushita K, Shibata Y et al (2023) A machine learning-based scoring system and ten factors associated with hip fracture occurrence in the elderly. Bone 176:116865. https://doi.org/10.1016/j.bone.2023.116865
Article CAS PubMed Google Scholar
Suri A, Jones BC, Ng G et al (2021) A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs. Bone 149:115972. https://doi.org/10.1016/j.bone.2021.115972
Article CAS PubMed PubMed Central Google Scholar
Zou KH, Tuncali K, Silverman SG (2003) Correlation and simple linear regression. Radiology 227(3):617–622. https://doi.org/10.1148/radiol.2273011499
Marill KA (2004) Advanced statistics: linear regression, part I: simple linear regression. Acad Emerg Med 11(1):87–93
Stoltzfus JC (2011) Logistic regression: a brief primer. Acad Emerg Med 18(10):1099–1104. https://doi.org/10.1111/j.1553-2712.2011.01185.x
Priebe CE, Marchette DJ, Healy DM (2004) Integrated sensing and processing decision trees. IEEE Trans Pattern Anal Mach Intell 26(6):699–708. https://doi.org/10.1109/tpami.2004.12
Winters-Hilt S, Merat S (2007) SVM clustering. BMC Bioinformatics 8 Suppl 7(Suppl 7):S18. https://doi.org/10.1186/1471-2105-8-S7-S18
Article CAS PubMed Google Scholar
Zhang Z (2016) Naïve Bayes classification in R. Ann Transl Med 4(12):241. https://doi.org/10.21037/atm.2016.03.38
Article CAS PubMed PubMed Central Google Scholar
Abu Alfeilat HA, Hassanat ABA, Lasassmeh O et al (2019) Effects of distance measure choice on K-nearest neighbor classifier performance: a review. Big Data 7(4):221–248. https://doi.org/10.1089/big.2018.0175
Breiman L (2001) Random forests. Mach Learn 2001(45):5–32
Xiaofei H, Shuicheng Y, Yuxiao H, Niyogi P, Hong-Jiang Z (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340. https://doi.org/10.1109/tpami.2005.55
Chen T, Guestrin C (2016) XGBoost. presented at: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Tran DT, Kiranyaz S, Gabbouj M, Iosifidis A (2020) Heterogeneous multilayer generalized operational perceptron. IEEE Trans Neural Netw Learn Syst 31(3):710–724. https://doi.org/10.1109/tnnls.2019.2914082
Zhang Q, Wang X, Cao R, Wu YN, Shi F, Zhu SC (2021) Extraction of an explanatory graph to interpret a CNN. IEEE Trans Pattern Anal Mach Intell 43(11):3863–3877. https://doi.org/10.1109/tpami.2020.2992207
Yang Y, Wu QMJ, Feng X, Akilan T (2020) Recomputation of the dense layers for performance improvement of DCNN. IEEE Trans Pattern Anal Mach Intell 42(11):2912–2925. https://doi.org/10.1109/tpami.2019.2917685
Sharif N, Gilani SZ, Suter D et al (2023) Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images. EBioMedicine 94:104676. https://doi.org/10.1016/j.ebiom.2023.104676
Article CAS PubMed PubMed Central Google Scholar
Schousboe JT, Lewis JR, Monchka BA et al (2024) Simultaneous automated ascertainment of prevalent vertebral fracture and abdominal aortic calcification in clinical practice: role in fracture risk assessment. J Bone Miner Res 39(7):898–905. https://doi.org/10.1093/jbmr/zjae066
Nakamura E, Miyao K, Ozeki T (1988) Assessment of biological age by principal component analysis. Mech Ageing Dev 46(1–3):1–18. https://doi.org/10.1016/0047-6374(88)90109-1
Article CAS PubMed Google Scholar
Park J, Cho B, Kwon H, Lee C (2009) Developing a biological age assessment equation using principal component analysis and clinical biomarkers of aging in Korean men. Arch Gerontol Geriatr Jul-Aug 49(1):7–12. https://doi.org/10.1016/j.archger.2008.04.003
Nakamura E, Miyao K (2003) Further evaluation of the basic nature of the human biological aging process based on a factor analysis of age-related physiological variables. J Gerontol A Biol Sci Med Sci 58(3):196–204. https://doi.org/10.1093/gerona/58.3.b196
Hofecker G, Skalicky M, Kment A, Niedermüller H (1980) Models of the biological age of the rat. I. A factor model of age parameters. Mech Ageing Dev 14(3–4):345–59. https://doi.org/10.1016/0047-6374(80)90008-1
Article CAS PubMed Google Scholar
Jones G, Willett P (1995) Docking small-molecule ligands into active sites. Curr Opin Biotechnol 6(6):652–656. https://doi.org/10.1016/0958-1669(95)80107-3
Article CAS PubMed Google Scholar
Kang SJ, Kim MJ, Hur Y-I
Comments (0)