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Related Experiment Video

Updated: Aug 5, 2025

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
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Hip Fracture Risk Assessment in Elderly and Diabetic Patients: Combining Autonomous Finite Element Analysis and

Zohar Yosibash1,2, Nir Trabelsi2,3, Itay Buchnik4

  • 1School of Mechanical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.

Journal of Bone and Mineral Research : the Official Journal of the American Society for Bone and Mineral Research
|March 27, 2023
PubMed
Summary

This study uses autonomous finite element analysis (AFE) and machine learning (ML) to predict hip fracture risk. The combined approach accurately assesses fracture risk in both type 2 diabetes mellitus (T2DM) and non-T2DM patients.

Keywords:
DIABETES MELLITUSFINITE ELEMENT ANALYSISFRACTURE RISK ASSESSMENTHIP FRACTURESVM/MACHINE LEARNING

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Area of Science:

  • Biomechanics
  • Medical Imaging
  • Machine Learning

Background:

  • Hip fractures pose a significant health risk, particularly in aging populations and individuals with type 2 diabetes mellitus (T2DM).
  • Current methods for assessing hip fracture risk have limitations in accuracy and patient-specific prediction.
  • Developing advanced predictive tools is crucial for effective fracture prevention strategies.

Purpose of the Study:

  • To develop and validate a machine learning (ML) algorithm combined with autonomous finite element analysis (AFE) for predicting hip fracture risk.
  • To assess the performance of this combined approach in both type 2 diabetes mellitus (T2DM) and non-T2DM patient populations.
  • To establish an opportunistic method for hip fracture risk assessment using readily available CT scan data.

Main Methods:

  • Retrospective clinical study utilizing CT scans of femurs from patients with and without hip fractures.
  • Autonomous finite element analysis (AFE) was performed on femur models derived from CT scans under physiological loads.
  • A support vector machine (SVM) ML algorithm was trained using AFE results, patient demographics, and fracture outcomes for risk prediction.

Main Results:

  • The AFE successfully analyzed 91% of appropriate femur scans (836 femurs).
  • The combined AFE-ML model demonstrated high prediction accuracy: 92% sensitivity and 88% specificity for T2DM patients (AUC 0.92), and 83% sensitivity and 84% specificity for non-T2DM patients (AUC 0.84).
  • The algorithm achieved excellent predictive performance, indicating its potential for clinical application.

Conclusions:

  • Combining AFE data with ML algorithms offers unprecedented accuracy in predicting hip fracture risk.
  • The fully autonomous algorithm can be implemented as an opportunistic tool for hip fracture risk assessment in diverse patient groups.
  • This approach holds significant promise for improving patient management and preventing hip fractures.