Skip to main content
SpringerLink
Log in

A Review of CT-Based Fracture Risk Assessment with Finite Element Modeling and Machine Learning

  • Orthopedic Management of Fractures (R Natoli and L Gerstenfeld, Section Editors)
  • Published:
Current Osteoporosis Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

We reviewed advances over the past 3 years in assessment of fracture risk based on CT scans, considering methods that use finite element models, machine learning, or a combination of both.

Recent Findings

Several studies have demonstrated that CT-based assessment of fracture risk, using finite element modeling or biomarkers derived from machine learning, is equivalent to currently used clinical tools. Phantomless calibration of CT scans for bone mineral density enables accurate measurements from routinely taken scans. This opportunistic use of CT scans for fracture risk assessment is facilitated by high-quality automated segmentation with deep learning, enabling workflows that do not require user intervention. Modeling of more realistic and diverse loading conditions, as well as improved modeling of fracture mechanisms, has shown promise to enhance our understanding of fracture processes and improve the assessment of fracture risk beyond the performance of current clinical tools.

Summary

CT-based screening for fracture risk is effective and, by analyzing scans that were taken for other indications, could be used to expand the pool of people screened, therefore improving fracture prevention. Finite element modeling and machine learning both provide valuable tools for fracture risk assessment. Future approaches should focus on including more loading-related aspects of fracture risk.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Schuit SCE, Van der Klift M, Weel A, De Laet C, Burger H, Seeman E, et al. Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone. 2004;34(1):195–202.

    Article  CAS  PubMed  Google Scholar 

  2. Wainwright SA, Marshall LM, Ensrud KE, Cauley JA, Black DM, Hillier TA, Hochberg MC, Vogt MT, Orwoll ES. Hip fracture in women without osteoporosis. J Clin Endocrinol Metab. 2005;90(5):2787–93.

    Article  CAS  PubMed  Google Scholar 

  3. Kanis JA, McCloskey EV, Johansson H, Oden A, Melton LJ III, Khaltaev N. A reference standard for the description of osteoporosis. Bone. 2008;42(3):467–75.

    Article  CAS  PubMed  Google Scholar 

  4. Kanis JA, Cooper C, Rizzoli R, Reginster JY. Executive summary of the European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Calcif Tissue Int. 2019;104(3):235–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cody DD, Gross GJ, Hou FJ, Spencer HJ, Goldstein SA, Fyhrie DP. Femoral strength is better predicted by finite element models than QCT and DXA. J Biomech [Internet]. 1999;32(10):1013–20. Available from: http://ac.els-cdn.com/S0021929099000998/1-s2.0-S0021929099000998-main.pdf?_tid=33b5b734-ae9a-11e5-9548-00000aacb35f&acdnat=1451441482_79796d7ca1594b74b3e1d6a2e4a2539b

  6. Crawford RP, Cann CE, Keaveny TM. Finite element models predict in vitro vertebral body compressive strength better than quantitative computed tomography. Bone. 2003;33(4):744–50.

    Article  PubMed  Google Scholar 

  7. Kopperdahl DL, Aspelund T, Hoffmann PF, Sigurdsson S, Siggeirsdottir K, Harris TB, et al. Assessment of incident spine and hip fractures in women and men using finite element analysis of CT scans. J Bone Miner Res [Internet]. 2014;29(3):570–80. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23956027

  8. Adams AL, Fischer H, Kopperdahl DL, Lee DC, Black DM, Bouxsein ML, et al. Osteoporosis and hip fracture risk from routine computed tomography scans: the fracture, osteoporosis, and CT utilization study (FOCUS). J Bone Miner Res. 2018;33(7):1291–301. Largest cohort study for fracture risk assessment with finite element-derived bone strength.

  9. Fleps I, Fung A, Guy P, Ferguson SJ, Helgason B, Cripton PA. Subject-specific ex vivo simulations for hip fracture risk assessment in sideways falls. Bone. 2019;125:36–45.

    Article  PubMed  Google Scholar 

  10. Hayes WC, Myers ER, Robinovitch SN, Van Den Kroonenberg A, Courtney AC, McMahon TA. Etiology and prevention of age-related hip fractures. Bone. 1996;18(1 Suppl):77S-86S.

  11. Mokhtarzadeh H, Anderson DE, Allaire BT, Bouxsein ML. Patterns of load-to-strength ratios along the spine in a population-based cohort to evaluate the contribution of spinal loading to vertebral fractures. J Bone Miner Res. 2021;36(4):704–11.

    Article  CAS  PubMed  Google Scholar 

  12. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

    Article  CAS  PubMed  Google Scholar 

  13. Chao Y-S, Sinclair A, Morrison A, Hafizi D, Pyke L. The Canadian Medical Imaging Inventory 2019–2020. 2021.

    Book  Google Scholar 

  14. Fleps I, Pálsson H, Baker A, Enns-Bray W, Bahaloo H, Danner M, et al. Finite element derived femoral strength is a better predictor of hip fracture risk than aBMD in the AGES Reykjavik study cohort. Bone. 2022;154:116219. Large cohort study that found improved fracture risk assessment compared to CT-based aBMD and compares different material implementations.

  15. Qasim M, Farinella G, Zhang J, Li X, Yang L, Eastell R, Viceconti M. Patient-specific finite element estimated femur strength as a predictor of the risk of hip fracture: the effect of methodological determinants. Osteoporos Int. 2016;27(9):2815–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Falcinelli C, Schileo E, Balistreri L, Baruffaldi F, Bordini B, Viceconti M, Albisinni U, Ceccarelli F, Milandri L, Toni A, Taddei F Multiple loading conditions analysis can improve the association between finite element bone strength estimates and proximal femur fractures: A preliminary study in elderly women. Bone [Internet]. 2014;67:71–80. Available from: http://linkinghub.elsevier.com/retrieve/pii/S8756328214002476

  17. Johannesdottir F, Allaire B, Kopperdahl DL, Keaveny TM, Sigurdsson S, Bredella MA, et al. Bone density and strength from thoracic and lumbar CT scans both predict incident vertebral fractures independently of fracture location. Osteoporos Int. 2021;32(2):261–9. Large cohort study on assessement of vertebral fracture risk based on CT-derived finite element models.

  18. Dieckmeyer M, Rayudu NM, Yeung LY, Löffler M, Sekuboyina A, Burian E, Sollmann N, Kirschke JS, Baum T, Subburaj K. Prediction of incident vertebral fractures in routine MDCT: Comparison of global texture features, 3D finite element parameters and volumetric BMD. Eur J Radiol. 2021;141:109827.

    Article  PubMed  Google Scholar 

  19. Allaire BT, Lu D, Johannesdottir F, Kopperdahl D, Keaveny TM, Jarraya M, Guermazi A, Bredella MA, Samelson EJ, Kiel DP, Anderson DE, Demissie S, Bouxsein ML. Prediction of incident vertebral fracture using CT-based finite element analysis. Osteoporos Int. 2019;30(2):323–31.

    Article  CAS  PubMed  Google Scholar 

  20. Services USD of H and H, Administration F and D. Reporting of Computational Modeling Studies in Medical Device Submissions [Internet]. 2016. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/reporting-computational-modeling-studies-medical-device-submissions

  21. Center for Devices and Radiological. Assessing Credibility of Computational Modeling through Verification and Validation: Application to Medical Devices [Internet]. 2018. Available from: https://www.asme.org/codes-standards/find-codes-standards/v-v-40-assessing-credibility-computational-modeling-verification-validation-application-medical-devices

  22. Lessmann N, Van Ginneken B, De Jong PA, Išgum I. Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med Image Anal. 2019;53:142–55.

    Article  PubMed  Google Scholar 

  23. Müller D, Kramer F. MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning. BMC Med Imaging. 2021;21(1):1–11.

    Article  Google Scholar 

  24. Sekuboyina A, Husseini ME, Bayat A, Löffler M, Liebl H, Li H, et al. VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images. Med Image Anal. 2021;73:102166. Comparison of state-of-the-art deep learning frameworks for image segmetation and labelling on a diverse dataset of the spine.

  25. Kim JJ, Nam J, Jang IG. Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm. Comput Methods Programs Biomed. 2018;154:161–71.

    Article  PubMed  Google Scholar 

  26. Väänänen SP, Grassi L, Venäläinen MS, Matikka H, Zheng Y, Jurvelin JS, Isaksson H. Automated segmentation of cortical and trabecular bone to generate finite element models for femoral bone mechanics. Med Eng Phys. 2019;70:19–28.

    Article  PubMed  Google Scholar 

  27. Hemke R, Buckless CG, Tsao A, Wang B, Torriani M. Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment. Skeletal Radiol. 2020;49(3):387–95. Study demonstrating muti-label segmentation with high accuracy for tissues in the pelvic region.

  28. Balagopal A, Kazemifar S, Nguyen D, Lin M-H, Hannan R, Owrangi A, Jiang S. Fully automated organ segmentation in male pelvic CT images. Phys Med Biol. 2018;63(24):245015.

    Article  PubMed  Google Scholar 

  29. Bjornsson PA, Baker A, Fleps I, Pauchard Y, Palsson H, Ferguson SJ, Sigurdsson S, Gudnason V, Helgason B, Ellingsen LM. Fast and robust femur segmentation from computed tomography images for patient-specific hip fracture risk screening. Computer Methods in Biomechanics and Biomedical Engineering: Imaging Vis. 2022:1–3. https://doi.org/10.1080/21681163.2022.2068160

  30. Pauchard Y, Fitze T, Browarnik D, Eskandari A, Pauchard I, Enns-Bray W, Pálsson H, Sigurdsson S, Ferguson SJ, Harris TB, Gudnason V, Helgason B. Interactive graph-cut segmentation for fast creation of finite element models from clinical ct data for hip fracture prediction. Comput Methods Biomech Biomed Engin. 2016;19(16):1693–703.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Winsor C, Li X, Qasim M, Henak CR, Pickhardt PJ, Ploeg H, et al. Evaluation of patient tissue selection methods for deriving equivalent density calibration for femoral bone quantitative CT analyses. Bone. 2021;143:115759. Study investigating the influence of different tissue combinations for phantomless calibration applied to hip BMD and finite element derived bone strength.

  32. Lee DC, Hoffmann PF, Kopperdahl DL, Keaveny TM. Phantomless calibration of CT scans for measurement of BMD and bone strength—inter-operator reanalysis precision. Bone. 2017;103:325–33. Study investating the inter-operator variability of phantomless calibration at the spine and hip.

  33. Bartenschlager S, Dankerl P, Chaudry O, Uder M, Engelke K. BMD accuracy errors specific to phantomless calibration of CT scans of the lumbar spine. Bone. 2022;157:116304. Study analysing the expected influence of tissue variability on BMD accuracty with phantomless calibration.

  34. Michalski AS, Besler BA, Michalak GJ, Boyd SK. CT-based internal density calibration for opportunistic skeletal assessment using abdominal CT scans. Med Eng Phys. 2020;78:55–63. Study comparing in-line, asynchronous and phantomless calibration for different applied to hip BMD and finite element derived bone strength.

  35. Prado M, Khosla S, Chaput C, Giambini H. Opportunistic application of phantom-less calibration methods for fracture risk prediction using QCT/FEA. Eur Radiol. 2021;31(12):9428–35. Study investigating the influence of different tissue combinations for phantomless calibration applied to vertebral BMD and finite element derived bone strength.

  36. Duchemin L, Mitton D, Jolivet E, Bousson V, Laredo JD, Skalli W. An anatomical subject-specific FE-model for hip fracture load prediction. Comput Methods Biomech Biomed Engin. 2008;11(2):105–11.

    Article  CAS  PubMed  Google Scholar 

  37. Bessho M, Ohnishi I, Matsuyama J, Matsumoto T, Imai K, Nakamura K. Prediction of strength and strain of the proximal femur by a CT-based finite element method. J Biomech [Internet]. 2006/10/13. 2007;40(8):1745–53. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17034798

  38. Ariza O, Gilchrist S, Widmer RP, Guy P, Ferguson SJ, Cripton PA, et al. Comparison of explicit finite element and mechanical simulation of the proximal femur during dynamic drop-tower testing. J Biomech [Internet]. 2015;48(2):224–32. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25527888

  39. Dragomir-Daescu D, Op Den Buijs J, McEligot S, Dai Y, Entwistle RC, Salas C, et al. Robust QCT/FEA models of proximal femur stiffness and fracture load during a sideways fall on the hip. Ann Biomed Eng. 2011;39(2):742–755.

  40. Melenk JM, Babuška I. The partition of unity finite element method: basic theory and applications. Comput Methods Appl Mech Eng. 1996;139(1–4):289–314.

    Article  Google Scholar 

  41. Miehe C, Welschinger F, Hofacker M. Thermodynamically consistent phase-field models of fracture: Variational principles and multi-field FE implementations. Int J Numer Methods Eng. 2010;83(10):1273–311.

    Article  Google Scholar 

  42. Shen R, Waisman H, Yosibash Z, Dahan G. A novel phase field method for modeling the fracture of long bones. Int j numer method biomed eng. 2019;35(8):e3211. First implementation of fracture predictions with the phase field methods in CT-based heterogeneous whole bone simulations.

  43. Gustafsson A, Tognini M, Bengtsson F, Gasser TC, Isaksson H, Grassi L. Subject-specific FE models of the human femur predict fracture path and bone strength under single-leg-stance loading. J Mech Behav Biomed Mater. 2021;113:104118. Implementation of XFEM into CT-based heteogeneous whole bone simulations with promising results for stance loading.

  44. Giambini H, Qin X, Dragomir-Daescu D, An K-N, Nassr A. Specimen-specific vertebral fracture modeling: a feasibility study using the extended finite element method. Med Biol Eng Comput. 2016;54(4):583–93.

    Article  PubMed  Google Scholar 

  45. Maghami E, Josephson TO, Moore JP, Rezaee T, Freeman TA, Karim L, et al. Fracture behavior of human cortical bone: Role of advanced glycation end-products and microstructural features. J Biomech. 2021;125:110600.

    Article  PubMed  Google Scholar 

  46. Wu C, Fang J, Zhang Z, Entezari A, Sun G, Swain MV, Li Q. Fracture modeling of brittle biomaterials by the phase-field method. Eng Fract Mech. 2020;224:106752.

    Article  Google Scholar 

  47. Navidtehrani Y, Betegón C, Martínez-Pañeda E. A simple and robust Abaqus implementation of the phase field fracture method. Appl Eng Sci. 2021;6:100050.

    Google Scholar 

  48. Molnár G, Gravouil A. 2D and 3D Abaqus implementation of a robust staggered phase-field solution for modeling brittle fracture. Finite Elem Anal Des. 2017;130:27–38. Study describing an open source Abaqus implementation of the phase field methods in a user defined element formuation.

  49. Yosibash Z, Trabelsi N, Hellmich C. Subject-specific p-FE analysis of the proximal femur utilizing micromechanics-based material properties. Int J Multiscale Comput Eng. 2008;6(5):483–98.

    Article  Google Scholar 

  50. Treece GM, Gee AH. Independent measurement of femoral cortical thickness and cortical bone density using clinical CT. Med Image Anal. 2015;20(1):249–64.

    Article  CAS  PubMed  Google Scholar 

  51. Chandran V, Maquer G, Gerig T, Zysset P, Reyes M. Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis. Med Image Anal. 2019;52:42–55.

    Article  PubMed  Google Scholar 

  52. Fleps I, Vuille M, Melnyk A, Ferguson SJ, Guy P, Helgason B, Cripton PA. A novel sideways fall simulator to study hip fractures ex vivo. PLoS One. 2018;13(7):e0201096.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Fleps I, Guy P, Ferguson SJ, Cripton PA, Helgason B. Explicit finite element models accurately predict subject-specific and velocity-dependent kinetics of sideways fall impact. J Bone Miner Res. 2019;34(10):1837–50. Validation study for finite element models that combine loading due to a fall with fracture predictions.

  54. Fleps I, Enns-Bray WS, Guy P, Ferguson SJ, Cripton PA, Helgason B. On the internal reaction forces, energy absorption, and fracture in the hip during simulated sideways fall impact. PLoS One. 2018;13(8).

  55. Enns-Bray WS, Bahaloo H, Fleps I, Pauchard Y, Taghizadeh E, Sigurdsson S, et al. Biofidelic finite element models for accurately classifying hip fracture in a retrospective clinical study of elderly women from the AGES Reykjavik cohort. Bone. 2019;120. Study that applies an impact model of the body for a sideways fall to a clinical cohort showing that fracture risk assessment for fallers could potentially be improved by incluing loading estimates into the finite element methodology.

  56. Galliker ES, Laing AC, Ferguson SJ, Helgason B, Fleps I. The influence of fall direction and hip protector on fracture risk: FE model predictions driven by experimental data. Ann Biomed Eng. 2022;50:1–13.

    Article  Google Scholar 

  57. Fung A, Fleps I, Cripton PA, Guy P, Ferguson SJ, Helgason B. Prophylactic augmentation implants in the proximal femur for hip fracture prevention: An in silico investigation of simulated sideways fall impacts. J Mech Behav Biomed Mater. 2022;126:104957.

    Article  CAS  PubMed  Google Scholar 

  58. Bhattacharya P, Altai Z, Qasim M, Viceconti M. A multiscale model to predict current absolute risk of femoral fracture in a postmenopausal population. Biomech Model Mechanobiol. 2018;18:1–18.

    Google Scholar 

  59. Anitha DP, Baum T, Kirschke JS, Subburaj K. Effect of the intervertebral disc on vertebral bone strength prediction: A finite-element study. Spine J. 2020;20(4):665–71. Study that demonstrates improved vertebral strength prediction when considering loading through the intervertebral disc compared to uniform displacement loading.

  60. Hussein AI, Louzeiro DT, Unnikrishnan GU, Morgan EF. Differences in trabecular microarchitecture and simplified boundary conditions limit the accuracy of quantitative computed tomography-based finite element models of vertebral failure. J Biomech Eng. 2018;140(2). The study highlight the imprtance of loading assumptions for the prediction of vertebral deformations during fracture with finite element models.

  61. Carberry GA, Pooler BD, Binkley N, Lauder TB, Bruce RJ, Pickhardt PJ. Unreported vertebral body compression fractures at abdominal multidetector CT. Radiology. 2013;268(1):120–6.

    Article  PubMed  Google Scholar 

  62. Genant HK, Wu CY, Van Kuijk C, Nevitt MC. Vertebral fracture assessment using a semiquantitative technique. J bone Miner Res. 1993;8(9):1137–48.

    Article  CAS  PubMed  Google Scholar 

  63. Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med. 2018;98:8–15. Automated vertebral fracture detection from CT scans with implications for its usefulness in clinical practice.

  64. Kolanu N, Silverstone EJ, Ho BH, Pham H, Hansen A, Pauley E, et al. Clinical utility of computer-aided diagnosis of vertebral fractures from computed tomography images. J Bone Miner Res. 2020;35(12):2307–12. Development and validation of the automated vertebral fracture detection model used in the commercial tool from Zebra Medical vision.

  65. Krishnaraj A, Barrett S, Bregman-Amitai O, Cohen-Sfady M, Bar A, Chettrit D, et al. Simulating dual-energy X-ray absorptiometry in CT using deep-learning segmentation cascade. J Am Coll Radiol. 2019;16(10):1473–9. Development and validation of the machine learning model for predicting DXA equivalent aBMD used in the commercial tool from Zebra Medical vision.

  66. Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. Eur Radiol. 2020;30(6):3549–57. Development and validation of the machine learning model for predicting DXA equivalent aBMD.

  67. Zhang M, Gong H, Zhang K. Prediction of lumbar vertebral strength of elderly men based on quantitative computed tomography images using machine learning. Osteoporos Int. 2019;30(11):2271–82.

    Article  CAS  PubMed  Google Scholar 

  68. Dagan N, Elnekave E, Barda N, Bregman-Amitai O, Bar A, Orlovsky M, et al. Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization. Nat Med [Internet]. 2020;26(1):77–82. Available from: https://doi.org/10.1038/s41591-019-0720-zApplication of machine learning based biomarkers (prevalent vertebreal fracture, DXA equivalent aBMD, vBMD) for retrospective fracture risk assessment in a large clincal cohort.

  69. Pickhardt PJ, Graffy PM, Zea R, Lee SJ, Liu J, Sandfort V, et al. Automated abdominal CT imaging biomarkers for opportunistic prediction of future major osteoporotic fractures in asymptomatic adults. Radiology. 2020;297(1):64–72. Application of machine learning based biomarkers (CT attenuation for bone, muscle, and viseral to subcutaneous fat ratio) for retrospective fracture risk assessment in a large clincal cohort.

  70. Liebl H, Schinz D, Sekuboyina A, Malagutti L, Löffler MT, Bayat A, el Husseini M, Tetteh G, Grau K, Niederreiter E, Baum T, Wiestler B, Menze B, Braren R, Zimmer C, Kirschke JS. A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data. Sci data. 2021;8(1):1–7.

    Article  Google Scholar 

  71. Newman HR, DeLucca JF, Peloquin JM, Vresilovic EJ, Elliott DM. Multiaxial validation of a finite element model of the intervertebral disc with multigenerational fibers to establish residual strain. JOR Spine. 2021;4(2):1–16.

    Article  Google Scholar 

  72. Wu Y, Loaiza J, Banerji R, Blouin O, Morgan E. Structure-function relationships of the human vertebral endplate. JOR spine. 2021;4(3):e1170.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Jackman TM, DelMonaco AM, Morgan EF. Accuracy of finite element analyses of CT scans in predictions of vertebral failure patterns under axial compression and anterior flexion. J Biomech. 2016;49(2):267–75.

    Article  PubMed  Google Scholar 

  74. Costa MC, Tozzi G, Cristofolini L, Danesi V, Viceconti M, Dall’Ara E. Micro finite element models of the vertebral body: validation of local displacement predictions. PLoS One. 2017;12(7):e0180151.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Yang Y, Komisar V, Shishov N, Lo B, Korall AMB, Feldman F, et al. The Effect of Fall Biomechanics on Risk for Hip Fracture in Older Adults: A Cohort Study of Video-Captured Falls in Long-Term Care. J Bone Miner Res. 2020;35(10):1914–22. Study reporting on the circumstances of falls in the elderly and how they relate to hip fractures.

Download references

Funding

This work was supported by grant AR054620 from the National Institutes of Health.

Author information

Authors and Affiliations

  1. College of Mechanical Engineering, Boston University, Boston, USA

    Ingmar Fleps & Elise F. Morgan

  2. Department of Biomedical Engineering, Boston University, Boston, MA, USA

    Elise F. Morgan

Authors
  1. Ingmar Fleps
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elise F. Morgan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ingmar Fleps.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Ethics Approval

This review article does not present any previously unpublished original research, and ethical approval is therefore not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Orthopedic Management of Fractures

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fleps, I., Morgan, E.F. A Review of CT-Based Fracture Risk Assessment with Finite Element Modeling and Machine Learning. Curr Osteoporos Rep 20, 309–319 (2022). https://doi.org/10.1007/s11914-022-00743-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11914-022-00743-w

Keywords

Search

Navigation