1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
Ferizi, U.; Honig, S.; Chang, G. Artificial intelligence, osteoporosis, and fragility fractures. Curr. Opin. Rheumatol. 2019, 31,
368–375. [CrossRef]
Peck, W.A. Consensus development conference: Diagnosis, prophylaxis, and treatment of osteoporosis. Am. J. Med. 1993, 94,
646–650.
Kanis, J.A. Diagnosis of osteoporosis and assessment of fracture risk. Lancet 2002, 359, 1929–1936. [CrossRef]
Kanis, J.A.; McCloskey, E.V.; Johansson, H.; Strom, O.; Borgstrom, F.; Oden, A. National Osteoporosis Guideline Group Case
finding for the management of osteoporosis with FRAX® —Assessment and intervention thresholds for the UK. Osteoporos. Int.
2008, 19, 1395–1408. [CrossRef] [PubMed]
Chen, J.H.; Asch, S.M. Machine Learning and Prediction in Medicine—Beyond the Peak of Inflated Expectations. New Engl. J.
Med. 2017, 376, 2507–2509. [CrossRef] [PubMed]
Koch, M. Artificial Intelligence Is Becoming Natural. Cell 2018, 173, 531–533. [CrossRef]
Madelin, G.; Poidevin, F.; Makrymallis, A.; Regatte, R.R. Classification of sodium MRI data of cartilage using machine learning.
Magn. Reson. Med. 2015, 74, 1435–1448. [CrossRef] [PubMed]
Kruse, C.; Eiken, P.; Vestergaard, P. Machine Learning Principles Can Improve Hip Fracture Prediction. Calcif. Tissue Int. 2017,
100, 348–360. [CrossRef]
Kruse, C.; Eiken, P.; Vestergaard, P. Clinical fracture risk evaluated by hierarchical agglomerative clustering. Osteoporos. Int. 2017,
28, 819–832. [CrossRef]
Villamor, E.; Monserrat, C.; Del Río, L.; Romero-Martín, J.; Rupérez, M. Prediction of osteoporotic hip fracture in postmenopausal
women through patient-specific FE analyses and machine learning. Comput. Methods Programs Biomed. 2020, 193, 105484.
[CrossRef] [PubMed]
Shioji, M.; Yamamoto, T.; Ibata, T.; Tsuda, T.; Adachi, K.; Yoshimura, N. Artificial neural networks to predict future bone mineral
density and bone loss rate in Japanese postmenopausal women. BMC Res. Notes 2017, 10, 590. [CrossRef]
Huang, C.-B.; Hu, J.-S.; Tan, K.; Zhang, W.; Xu, T.-H.; Yang, L. Application of machine learning model to predict osteoporosis
based on abdominal computed tomography images of the psoas muscle: A retrospective study. BMC Geriatr. 2022, 22, 796.
[CrossRef]
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.;
et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830.
Matsuo, K.; Aihara, H.; Nakai, T.; Morishita, A.; Tohma, Y.; Kohmura, E. Machine Learning to Predict In-Hospital Morbidity and
Mortality after Traumatic Brain Injury. J. Neurotrauma 2020, 37, 202–210. [CrossRef]
Brunelli, A.; Rocco, G. Internal validation of risk models in lung resection surgery: Bootstrap versus training-and-test sampling. J.
Thorac. Cardiovasc. Surg. 2006, 131, 1243–1247. [CrossRef]
Crandall, C.J.; Ensrud, K.E. Osteoporosis Screening in Younger Postmenopausal Women. JAMA 2020, 323, 367–368. [CrossRef]
[PubMed]
Koh, L.K.H.; Ben Sedrine, W.; Torralba, T.P.; Kung, A.; Fujiwara, S.; Chan, S.P.; Huang, Q.R.; Rajatanavin, R.; Tsai, K.-S.; Park, H.M.;
et al. A Simple Tool to Identify Asian Women at Increased Risk of Osteoporosis. Osteoporos. Int. 2001, 12, 699–705. [CrossRef]
[PubMed]
Bioengineering 2023, 10, 277
18.
19.
20.
21.
22.
23.
24.
25.
26.
10 of 10
Bui, H.M.; Ha, M.H.; Pham, H.G.; Dao, T.P.; Nguyen, T.-T.T.; Nguyen, M.L.; Vuong, N.T.; Hoang, X.H.T.; Do, L.T.; Dao, T.X.; et al.
Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches. Sci. Rep. 2022, 12, 20160.
[CrossRef]
Erjiang, E.; Wang, T.; Yang, L.; Dempsey, M.; Brennan, A.; Yu, M.; Chan, W.P.; Whelan, B.; Silke, C.; O’Sullivan, M.; et al. Machine
Learning Can Improve Clinical Detection of Low BMD: The DXA-HIP Study. J. Clin. Densitom. 2020, 24, 527–537. [CrossRef]
Ou Yang, W.Y.; Lai, C.C.; Tsou, M.T.; Hwang, L.C. Development of Machine Learning Models for Prediction of Osteoporosis from
Clinical Health Examination Data. Int. J. Environ. Res. Public Health 2021, 18, 7635. [CrossRef] [PubMed]
Yan, J.; Xu, Y.; Cheng, Q.; Jiang, S.; Wang, Q.; Xiao, Y.; Ma, C.; Yan, J.; Wang, X. LightGBM: Accelerated genomically designed crop
breeding through ensemble learning. Genome Biol. 2021, 22, 271. [CrossRef] [PubMed]
Shimizu, T.; Suda, K.; Maki, S.; Koda, M.; Harmon, S.M.; Komatsu, M.; Ota, M.; Ushirozako, H.; Minami, A.; Takahata, M.;
et al. Efficacy of a machine learning-based approach in predicting neurological prognosis of cervical spinal cord injury patients
following urgent surgery within 24 h after injury. J. Clin. Neurosci. 2022, 107, 150–156. [CrossRef] [PubMed]
Ahamad, M.; Aktar, S.; Uddin, J.; Rahman, T.; Alyami, S.A.; Al-Ashhab, S.; Akhdar, H.F.; Azad, A.; Moni, M.A. Early-Stage
Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches. J. Pers. Med. 2022, 12, 1211. [CrossRef]
[PubMed]
Tsai, I.-J.; Shen, W.-C.; Lee, C.-L.; Wang, H.-D.; Lin, C.-Y. Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory
Data. Diagnostics 2022, 12, 203. [CrossRef]
Breitling, L.P. Liver enzymes and bone mineral density in the general population. J. Clin. Endocrinol. Metab. 2015, 100, 3832–3840.
[CrossRef]
Kim, J.; Kim, H.S.; Lee, H.S.; Kwon, Y.-J. The relationship between platelet count and bone mineral density: Results from two
independent population-based studies. Arch. Osteoporos. 2020, 15, 43. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
...