Study of the significance of parameters and their interaction on assessing femoral fracture risk by quantitative statistical analysis-Reference-Cited by-同舟云学术

Study of the significance of parameters and their interaction on assessing femoral fracture risk by quantitative statistical analysis

Published:2022-02-04 Issue:3 Volume:60 Page:843-854
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Awal Rabina,Ben Hmida Jalel,Luo Yunhua,Faisal Tanvir^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-022-02516-0.pdf

Reference63 articles.

1. Lee Y, Ogihara N, Lee TJ (2019) Assessment of finite element models for prediction of osteoporotic fracture. J Mech Behav Biomed Mater 97:312–320

2. Szulc P, Marchand F, Duboeuf F, Delmas PJ (2000) Cross-sectional assessment of age-related bone loss in men: the MINOS study. Bone 26(2):123–129

3. Bettamer A (2013) prediction of proximal femur fracture: finite element modeling based on mechanical damage and experimental validation.

4. Group RoaWS (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. HO Technical Report Series, vol 843. World Health Organization, Geneva

5. Organization WH WHO scientific group on the assessment of osteoporosis at primary health care level: summary meeting report 2004. Belgium WHO:5–7

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Force magnitude and distribution during impacts to the hip are affected differentially by body size and body composition;Journal of Biomechanics;2024-07

2. Risk analysis for subsequent fracture of osteoporotic fractures in Chinese women over age 60: a nationwide cross-sectional study;Scientific Reports;2024-06-10

3. QCT-based 3D finite element modeling to assess patient-specific hip fracture risk and risk factors;Journal of the Mechanical Behavior of Biomedical Materials;2024-02

4. SSDL—an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images;Medical & Biological Engineering & Computing;2024-01-13

5. Semi-Supervised Learning Based Femur Segmentation from QCT Images;2023 International Conference on Machine Learning and Applications (ICMLA);2023-12-15