Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review

참고문헌 (21)

1. Johnell O, "An estimate of the worldwide prevalence and disability associated with osteoporotic fractures", Osteoporos Int, 17, pp.1726~1733 (2006)[DOI]
2. Sànchez-Riera L, "The global burden attributable to low bone mineral density", Ann Rheum Dis, 73, pp.1635~1645 (2014)[DOI]
3. Saito T, "Effectiveness of anti-osteoporotic drugs to prevent secondary fragility fractures : systematic review and meta-analysis", Osteoporos Int, 28, pp.3289~3300 (2017)[DOI]
4. 차용한, "Introduction of the Cost-Effectiveness Studies of Fracture Liaison Service in Other Countries", 대한골대사학회지, 27(2), pp.79~83 (2020), 대한골대사학회
5. 차용한, "Establishment of Fracture Liaison Service in Korea: Where Is It Stand and Where Is It Going?", 대한골대사학회지, 26(4), pp.207~211 (2019), 대한골대사학회
6. LeBoff MS, "The clinician’s guide to prevention and treatment of osteoporosis", Osteoporos Int, 33, pp.2049~2102 (2022)[DOI]
7. Cheng CT, "A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs", Nat Commun, 12, pp.1066~ (2021)[DOI]
8. Hsieh CI, "Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning", Nat Commun, 12, pp.5472~ (2021)[DOI]
9. Basheer IA, "Artificial neural networks: fundamentals, computing, design, and application", J Microbiol Methods, 43, pp.3~31 (2000)[DOI]
10. Patel JL, "Applications of artificial neural networks in medical science", Curr Clin Pharmacol, 2, pp.217~226 (2007)[DOI]
11. de Vries BCS, "Comparing three machine learning approaches to design a risk assessment tool for future fractures : predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis", Osteoporos Int, 32, pp.437~449 (2021)[DOI]
12. Villamor E, "Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning", Comput Methods Programs Biomed, 193, pp.105484~ (2020)[DOI]
13. Ho-Le TP, "Prediction of hip fracture in post-menopausal women using artificial neural network approach", Annu Int Conf IEEE Eng Med Biol Soc, 2017, pp.4207~4210 (2017)
14. Jiang P, "Fusion of clinical and stochastic finite element data for hip fracture risk prediction", J Biomech, 48, pp.4043~4052 (2015)[DOI]
15. Engels A, "Osteoporotic hip fracture prediction from risk factors available in administrative claims data-A machine learning approach", PLoS One, 15, pp.e0232969~ (2020)[DOI]
16. Kruse C, "Machine learning principles can improve hip fracture prediction", Calcif Tissue Int, 100, pp.348~360 (2017)[DOI]
17. Tseng WJ, "Hip fracture risk assessment : artificial neural network outperforms conditional logistic regression in an age-and sex-matched case control study", BMC Musculoskelet Disord, 14, pp.207~ (2013)[DOI]
18. Nazrun AS, "A systematic review of the outcomes of osteoporotic fracture patients after hospital discharge: morbidity, subsequent fractures, and mortality", Ther Clin Risk Manag, 10, pp.937~948 (2014)[DOI]
19. Marks R, "Hip fracture epidemiological trends, outcomes, and risk factors, 1970-2009", Int J Gen Med, 3, pp.1~17 (2010)
20. Aldieri A, "Improving the hip fracture risk prediction with a statistical shape-and-intensity model of the proximal femur", Ann Biomed Eng, 50, pp.211~221 (2022)[DOI]
21. Baker-LePain JC, "Active shape modeling of the hip in the prediction of incident hip fracture", J Bone Miner Res, 26, pp.468~474 (2011)[DOI]

관련 논문AI추천