Presentation Information
[450102-02-01]Autonomous finite element analysis of fracture prediction in human bones applied in clinical practice
Prof. Zohar Yosibash (Tel Aviv University)
The prescription of medications for individuals at risk of osteoporotic hip fractures, or the recommendation of prophylactic surgery for patients with femoral metastases, requires reliable, validated, patient-specific finite element analyses (FEAs). Traditionally, these analyses have been challenging to automate due to their complexity, including geometry generation from CT scans, the application of heterogeneous bone material properties, complex failure laws, and physiological boundary conditions.
Recent advancements such as low-dose CT scanning, machine learning, and high-order FEAs with inherent accuracy verification now enable a fully autonomous approach to evaluating bone strength and fracture risk. This new approach, termed autonomous finite element (AFE) analysis, represents a paradigm shift in FEA application.
This presentation introduces a novel patient-specific AFE process for femurs, designed for clinical practice. It encompasses automatic femur segmentation from CT scans using U-Net, automated mesh generation and boundary condition applications based on anatomical markers, high-order FE analysis with numerical error control, and an automatically generated report that clearly assesses fracture risk.
Two clinical applications of AFE will be demonstrated: (a) Quantifying fracture risk in patients with femoral tumors, along with surgical recommendations, and (b) opportunistically identifying patients at high risk for hip fractures.
Recent advancements such as low-dose CT scanning, machine learning, and high-order FEAs with inherent accuracy verification now enable a fully autonomous approach to evaluating bone strength and fracture risk. This new approach, termed autonomous finite element (AFE) analysis, represents a paradigm shift in FEA application.
This presentation introduces a novel patient-specific AFE process for femurs, designed for clinical practice. It encompasses automatic femur segmentation from CT scans using U-Net, automated mesh generation and boundary condition applications based on anatomical markers, high-order FE analysis with numerical error control, and an automatically generated report that clearly assesses fracture risk.
Two clinical applications of AFE will be demonstrated: (a) Quantifying fracture risk in patients with femoral tumors, along with surgical recommendations, and (b) opportunistically identifying patients at high risk for hip fractures.