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

Overview

Journal J Bone Metab

Date 2023 Sep 18

PMID 37718902

Authors

Yonghan Cha

Jung-Taek Kim

Jin-Woo Kim

Sung Hyo Seo

Sang-Yeob Lee

Jun-Il Yoo

Affiliations

Soon will be listed here.

Abstract

Background: Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology.

Methods: The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including "hip fractures" AND "artificial intelligence".

Results: A total of 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%.

Conclusions: We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.

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References

Johnell O, Kanis J . An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int. 2006; 17(12):1726-33. DOI: 10.1007/s00198-006-0172-4. View

Ho-Le T, Center J, Eisman J, Nguyen T, Nguyen H . Prediction of hip fracture in post-menopausal women using artificial neural network approach. Annu Int Conf IEEE Eng Med Biol Soc. 2017; 2017:4207-4210. DOI: 10.1109/EMBC.2017.8037784. View

de Vries B, Hegeman J, Nijmeijer W, Geerdink J, Seifert C, Groothuis-Oudshoorn C . 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. 2021; 32(3):437-449. DOI: 10.1007/s00198-020-05735-z. View

Aldieri A, Bhattacharya P, Paggiosi M, Eastell R, Audenino A, Bignardi C . Improving the Hip Fracture Risk Prediction with a Statistical Shape-and-Intensity Model of the Proximal Femur. Ann Biomed Eng. 2022; 50(2):211-221. PMC: 8803671. DOI: 10.1007/s10439-022-02918-z. View

Patel J, Goyal R . Applications of artificial neural networks in medical science. Curr Clin Pharmacol. 2008; 2(3):217-26. DOI: 10.2174/157488407781668811. View

Sanchez-Riera L, Carnahan E, Vos T, Veerman L, Norman R, Lim S . The global burden attributable to low bone mineral density. Ann Rheum Dis. 2014; 73(9):1635-45. DOI: 10.1136/annrheumdis-2013-204320. View

Cha Y, Ha Y, Lim J . Establishment of Fracture Liaison Service in Korea: Where Is It Stand and Where Is It Going?. J Bone Metab. 2019; 26(4):207-211. PMC: 6901692. DOI: 10.11005/jbm.2019.26.4.207. View

Tseng W, Hung L, Shieh J, Abbod M, Lin J . Hip fracture risk assessment: artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study. BMC Musculoskelet Disord. 2013; 14:207. PMC: 3723443. DOI: 10.1186/1471-2474-14-207. View

Engels A, Reber K, Lindlbauer I, Rapp K, Buchele G, Klenk J . Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach. PLoS One. 2020; 15(5):e0232969. PMC: 7237034. DOI: 10.1371/journal.pone.0232969. View

10.

Kruse C, Eiken P, Vestergaard P . Machine Learning Principles Can Improve Hip Fracture Prediction. Calcif Tissue Int. 2017; 100(4):348-360. DOI: 10.1007/s00223-017-0238-7. View

11.

Marks R . Hip fracture epidemiological trends, outcomes, and risk factors, 1970-2009. Int J Gen Med. 2010; 3:1-17. PMC: 2866546. View

12.

Jiang P, Missoum S, Chen Z . Fusion of clinical and stochastic finite element data for hip fracture risk prediction. J Biomech. 2015; 48(15):4043-4052. PMC: 4737502. DOI: 10.1016/j.jbiomech.2015.09.044. View

13.

Basheer I, Hajmeer M . Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods. 2000; 43(1):3-31. DOI: 10.1016/s0167-7012(00)00201-3. View

14.

Cha Y, Ha Y, Lim J, Kim W . Introduction of the Cost-Effectiveness Studies of Fracture Liaison Service in Other Countries. J Bone Metab. 2020; 27(2):79-83. PMC: 7297624. DOI: 10.11005/jbm.2020.27.2.79. View

15.

Nazrun A, Tzar M, Mokhtar S, Naina Mohamed I . A systematic review of the outcomes of osteoporotic fracture patients after hospital discharge: morbidity, subsequent fractures, and mortality. Ther Clin Risk Manag. 2014; 10:937-48. PMC: 4242696. DOI: 10.2147/TCRM.S72456. View

16.

Hsieh C, Zheng K, Lin C, Mei L, Lu L, Li W . Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning. Nat Commun. 2021; 12(1):5472. PMC: 8446034. DOI: 10.1038/s41467-021-25779-x. View

17.

Villamor E, Monserrat C, Del Rio L, Romero-Martin J, Ruperez M . Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Comput Methods Programs Biomed. 2020; 193:105484. DOI: 10.1016/j.cmpb.2020.105484. View

18.

LeBoff M, Greenspan S, Insogna K, Lewiecki E, Saag K, Singer A . The clinician's guide to prevention and treatment of osteoporosis. Osteoporos Int. 2022; 33(10):2049-2102. PMC: 9546973. DOI: 10.1007/s00198-021-05900-y. View

19.

Saito T, Sterbenz J, Malay S, Zhong L, MacEachern M, Chung K . Effectiveness of anti-osteoporotic drugs to prevent secondary fragility fractures: systematic review and meta-analysis. Osteoporos Int. 2017; 28(12):3289-3300. DOI: 10.1007/s00198-017-4175-0. View

20.

Cheng C, Wang Y, Chen H, Hsiao P, Yeh C, Hsieh C . A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs. Nat Commun. 2021; 12(1):1066. PMC: 7887334. DOI: 10.1038/s41467-021-21311-3. View