# A patient-specific computational technique to predict spine injury risks associated with physical activities

> **NIH NIH K25** · MAYO CLINIC ROCHESTER · 2024 · $148,257

## Abstract

PROJECT SUMMARY/ABSTRACT
Vertebral fracture is the most common type of osteoporotic fracture. Spine is also the most common site of
bone metastasis, leading to pathologic vertebral fractures. While performing activities of daily living is an
essential part of healthy aging, pathologic and non-pathologic vertebral fractures can occur during these
activities in metastatic or osteoporotic spines. These fractures cause pain and neurologic manifestations,
affecting quality of life. Currently, there is no objective clinical technique that can assess bone fracture risks
associated with physical activities. To fill the gap, we propose a patient-specific computational technique to
quantitatively evaluate spine injury risks associated with physical activities. This novel approach will enable
clinicians to reliably recommend safe and individualized activities to elderly populations. To achieve this, we
will obtain motions and muscle activity outcomes from an elderly patient cohort using video motion analysis.
These data will be used as input for kinematic motion analyses and mechanical testing on cadaveric lumbar
spines, to create and validate our computational models. The rationale for this project is that a QCT/FEA
process that can mimic physical activities will be able to reduce vertebral fractures and improve quality of life in
elderly patient populations. Our long-term goal is to develop reliable computational techniques to enable earlier
injury risk predictions in elderly patients with musculoskeletal diseases. Our overall objective, in this
application, is to develop a patient-specific quantitative computed tomography-based finite element analysis
(QCT/FEA) method that can assess both kinematic motions and fracture characteristics of the spine, to
estimate fracture risks of physical activities. To achieve the overall objective, the following three independent
specific aims will be accomplished: 1) to obtain lumbar range of motion and muscle response outcomes in an
elderly patient population during five physical movements; 2) to perform kinematic testing on cadaveric spines
to measure intradiscal pressures –using a novel approach– during physical movements; and also mechanical
testing on spine segments to measure intradiscal pressure at fracture; and 3) to develop and validate
QCT/FEA models of the lumbar spine to estimate spine injury risks.
This research will lead to the development
of a computational tool that can assign a risk score associated with physical activities.
 Further, this work will
provide preliminary data for future R01 grant proposals to predict fracture risks associated with physical
movements and exercises in patient populations.

## Key facts

- **NIH application ID:** 10817036
- **Project number:** 5K25AG068368-04
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Asghar Rezaei
- **Activity code:** K25 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $148,257
- **Award type:** 5
- **Project period:** 2021-04-15 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10817036

## Citation

> US National Institutes of Health, RePORTER application 10817036, A patient-specific computational technique to predict spine injury risks associated with physical activities (5K25AG068368-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10817036. Licensed CC0.

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