The paper focuses on establishing a risk assessment model of femoral neck osteoporotic fracture (FNOF) in the elderly population and improving the screening efficiency and accuracy of such diseases in specific populations. In literature research, the main risk factors of femoral neck osteoporosis (FNOP) in the elderly were studied and analyzed; the femur region of interest (ROI) and the hard bone edge segmentation model were selected from the X-ray digital image by using the image depth learning method. On this basis, the femoral trabecular score and femoral neck strength (FNS) in the set region were selected as the main evaluation elements, and the quantitative analysis method was established; an X-ray image processing method was applied to the feasibility study of FNOP and compared with dual-energy X-ray absorptiometry measurements of bone mineral density; Finally, the main risk factors of FNOP were selected and the prediction model of FNOP in the elderly population was established based on medical image processing, machine learning model construction and other methods. Some FNOP health records were selected as test samples for comparative analysis with traditional manual evaluation methods. The paper shows the risk assessment model of FNOF in the elderly population, which is feasible in testing. Among them, the artificial neural network model had a better accuracy (95.83%) and recall rate (100.00%), and the support vector machine prediction model had high specificity (62.50%). With the help of a machine learning method to establish the risk assessment model of FNOF for the elderly, one can provide decision support for the fracture risk assessment of the elderly and remind the clinic to give targeted interventions for the above high-risk groups in order to reduce the fracture risk.
Citation: Juan Du, Junying Wang, Xinghui Gai, Yan Sui, Kang Liu, Dewu Yang. Application of intelligent X-ray image analysis in risk assessment of osteoporotic fracture of femoral neck in the elderly[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 879-893. doi: 10.3934/mbe.2023040
The paper focuses on establishing a risk assessment model of femoral neck osteoporotic fracture (FNOF) in the elderly population and improving the screening efficiency and accuracy of such diseases in specific populations. In literature research, the main risk factors of femoral neck osteoporosis (FNOP) in the elderly were studied and analyzed; the femur region of interest (ROI) and the hard bone edge segmentation model were selected from the X-ray digital image by using the image depth learning method. On this basis, the femoral trabecular score and femoral neck strength (FNS) in the set region were selected as the main evaluation elements, and the quantitative analysis method was established; an X-ray image processing method was applied to the feasibility study of FNOP and compared with dual-energy X-ray absorptiometry measurements of bone mineral density; Finally, the main risk factors of FNOP were selected and the prediction model of FNOP in the elderly population was established based on medical image processing, machine learning model construction and other methods. Some FNOP health records were selected as test samples for comparative analysis with traditional manual evaluation methods. The paper shows the risk assessment model of FNOF in the elderly population, which is feasible in testing. Among them, the artificial neural network model had a better accuracy (95.83%) and recall rate (100.00%), and the support vector machine prediction model had high specificity (62.50%). With the help of a machine learning method to establish the risk assessment model of FNOF for the elderly, one can provide decision support for the fracture risk assessment of the elderly and remind the clinic to give targeted interventions for the above high-risk groups in order to reduce the fracture risk.
| [1] |
S. Khosla, L. C. Hofbauer, Osteoporosis treatment: recent developments and ongoing challenges. Lancet Diabetes Endocrinol., 5 (2017), 898–907. https://doi.org/10.1016/s2213-8587(17)30188-2 doi: 10.1016/S2213-8587(17)30188-2
|
| [2] |
W. Chen, H. Lv, S. Liu, Bo Liu, Y. Zhu, X. Chen, et al., National incidence of traumatic fractures in China: a retrospective survey of 512187 individuals, Lancet Glob. Health, 5 (2017), 807–817. https://doi.org/10.1016/s2214-109x(17)30222-x doi: 10.1016/S2214-109X(17)30222-X
|
| [3] |
J. Compston, A. Cooper, C. Cooper, N. Gittoes, C. Gregson, N. Harvey, et al., UK clinical guideline for the prevention and treatment of osteoporosis. Arch. Osteoporos., 12 (2017). https://doi.org/10.1007/s11657-017-0324-5 doi: 10.1007/s11657-017-0324-5
|
| [4] |
S. J. Curry, A. H. Krist, D. K. Owens, M. J. Barry, A. B. Caughey, K. W. Davidson, et al., Screening for osteoporosis to prevent fractures: US preventive services task force recommendation statement, JAMA, 319 (2018), 2521–2531. https://doi.org/10.1001/jama.2018.7498 doi: 10.1001/jama.2018.7498
|
| [5] |
E. M. Lewiecki, New and emerging concepts in the use of denosumab for the treatment of osteoporosis, Ther. Adv. Musculoskelet. Dis., 10 (2018), 209–223. https://doi.org/10.1177/1759720x18805759 doi: 10.1177/1759720X18805759
|
| [6] |
K. H. Rubin, S. Möller, T. Holmberg, M. Bliddal, J. Søndergaard, B. Abrahamsen, A new fracture risk assessment tool (FREM) based on public health registries, J. Bone Miner. Res., 33 (2018), 1967–1979. https://doi.org/10.1002/jbmr.3528 doi: 10.1002/jbmr.3528
|
| [7] |
Y. T. Lagerros, E. Hantikainen, K. Michaëlsson, W. Ye, H. Adami, R. Bellocco, Physical activity and the risk of hip fracture in the elderly: a prospective cohort study, Eur. J. Epidemiol., 32 (2017), 983–991. https://doi.org/10.1007/s10654-017-0312-5 doi: 10.1007/s10654-017-0312-5
|
| [8] |
R. J. Valderrábano, J. Lee, L. Lui, A. R. Hoffman, S. R. Cummings, E. S. Orwoll, Older men with anemia have increased fracture risk independent of bone mineral density, J. Clin. Endocrinol. Metab., 102 (2017), 2199–2206. https://doi.org/10.1210/jc.2017-00266 doi: 10.1210/jc.2017-00266
|
| [9] |
J. A. Kanis, C. Cooper, R. Rizzoli, J. Reginster, European guidance for the diagnosis and management of osteoporosis in postmenopausal women, Osteoporos. Int., 30 (2019), 43–44. https://doi.org/10.1007/s00198-018-4704-5 doi: 10.1007/s00198-018-04811-9
|
| [10] | N. Hollevoet, R. Verdonk, J. Kaufman, S. Goemaere, Osteoporotic fracture treatment, Acta. Orthop. Belg., 77 (2011), 441–447. |
| [11] |
J. Hippisley-Cox, C. Coupland, Predicting risk of osteoporotic fracture in men and women in England and Wales: prospective derivation and validation of QFractureScores, BMJ, (2009). https://doi.org/10.1136/bmj.b4229 doi: 10.1136/bmj.b4229
|
| [12] |
S. D. Berry, E. J. Samelson, M. J. Pencina, R. R. McLean, A. Cupples, K. E. Broe, et al., Repeat BMD screening and prediction of fracture, JAMA, 310 (2013), 1256–1262. https://doi.org/10.1001/jama.2013.277817 doi: 10.1001/jama.2013.277817
|
| [13] |
G. Ioannidis, A. Papaioannou, L. Thabane, A. Gafni, A. Hodsman, B. Kvern, et al., Family physicians' personal and practice characteristics that are associated with improved utilization of bone mineral density testing and osteoporosis medication prescribing, Popul. Health Manag., 12 (2009). https://doi.org/10.1089/pop.2008.0025 doi: 10.1089/pop.2008.0025
|
| [14] |
J. C. Hyer, D. E. Deas, A. A. Palaiologou, M. E. Noujeim, M. J. Mader, B. L Mealey, Accuracy of dental calculus detection using digital radiography and image manipulation, J. Periodontal., 92 (2021), 419–427. https://doi.org/10.1002/jper.19-0669 doi: 10.1002/JPER.19-0669
|
| [15] |
F. An, J. Liu, Medical image segmentation algorithm based on multilayer boundary perception-self attention deep learning model, Multimed. Tools Appl., 80 (2021), 15017–15039. https://doi.org/10.1007/s11042-021-10515-w doi: 10.1007/s11042-021-10515-w
|
| [16] |
Q. Shi, S. Yin, K. Wang, L. Teng, H. Li, Multichannel convolutional neural network-based fuzzy active contour model for medical image segmentation, Evol. Syst., 13 (2022), 535–549. https://doi.org/10.1007/s12530-021-09392-3 doi: 10.1007/s12530-021-09392-3
|
| [17] |
S. D. Berry, A. R. Zullo, Y. Lee, V. Mor, K. W. McConeghy, G. Banerjee, et al., Fracture Risk Assessment in Long-term Care (FRAiL): development and validation of a prediction model, J. Gerontol. A Biol. Sci. Med. Sci., 73 (2018), 763–769. https://doi.org/10.1093/gerona/glx147 doi: 10.1093/gerona/glx147
|
| [18] |
L. Shepstone, E. Lenaghan, C. Cooper, S. Clarke, R. Fong-Soe-Khioe, R. Fordham, Screening in the community to reduce fractures in older women (SCOOP): a randomised controlled trial, Lancet, 391 (2018), 741–747. https://doi.org/10.1016/s0140-6736(17)32640-5 doi: 10.1016/S0140-6736(17)32640-5
|
| [19] |
N. Dagan, C. Cohen-Stavi, M. Leventer-Roberts, R. D. Balicer, External validation and comparison of three prediction tools for risk of osteoporotic fractures using data from population based electronic health records: retrospective cohort study, BMJ, (2017). https://doi.org/10.1136/bmj.i6755 doi: 10.1136/bmj.i6755
|
| [20] |
G. E. Fuleihan, M. Chakhtoura, J. A. Cauley, N. Chamoun, Worldwide Fracture Prediction, J. Clin. Densitom., 20 (2017), 397–424. https://doi.org/10.1016/j.jocd.2017.06.008 doi: 10.1016/j.jocd.2017.06.008
|
| [21] |
C. M. Rao, P. Singh, D. Maikap, P. Padhan, Musculoskeletal disorders in chronic obstructive airway diseases: a neglected clinical entity, Mediterr. J. Rheumatol., 32 (2021), 118–123. https://doi.org/10.31138/mjr.32.2.118 doi: 10.31138/mjr.32.2.118
|
| [22] | M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, V. K. Asari, Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation, preprint, arXiv: 1802.06955. |
| [23] |
Y. Su, J. Leung, T. Kwok, The role of previous falls in major osteoporotic fracture prediction in conjunction with FRAX in older Chinese men and women: the Mr. OS and Ms. OS cohort study in Hong Kong, Osteoporos. Int., 29 (2018), 355–363. https://doi.org/10.1007/s00198-017-4277-8 doi: 10.1007/s00198-017-4277-8
|
| [24] |
H. Johansson, S. S. Dela, B. Cassim, F. Paruk, S. L. Brown, M. Conradie, et al., FRAX-based fracture probabilities in South Africa, Arch. Osteoporos., 16 (2021), 51. https://doi.org/10.1007/s11657-021-00905-w doi: 10.1007/s11657-021-00905-w
|
| [25] |
M. K. Skjødt, S. Möller, N. Hyldig, A. Clausen, M. Bliddal, J. Søndergaard, et al., Validation of the Fracture Risk Evaluation Model (FREM) in predicting major osteoporotic fractures and hip fractures using administrative health data, Bone, 147 (2021), 115934. https://doi.org/10.1016/j.bone.2021.115934 doi: 10.1016/j.bone.2021.115934
|
Figures(3) / Tables(5)
Juan Du, Junying Wang, Xinghui Gai, Yan Sui, Kang Liu, Dewu Yang. Application of intelligent X-ray image analysis in risk assessment of osteoporotic fracture of femoral neck in the elderly[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 879-893. doi: 10.3934/mbe.2023040
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