# Emerging Technologies for Early Detection of Distal Leg Stress Fracture.

> **NIH NIH P20** · BOISE STATE UNIVERSITY · 2023 · $203,323

## Abstract

PROJECT SUMMARY – BROWN
Stress Fracture is a common and highly destructive overuse musculoskeletal injury that may be successfully
treated with a brief reduction in physical activity. Yet, we currently lack the scientific knowledge and technical
capacity to accurately assess bone damage in time to allow practitioners an opportunity to prescribe the rest
necessary to avoid fracture development. The long-term goal is to enhance scientific knowledge of stress
fracture development, and improve researcher and clinician ability to predict and accurately detect individuals
at risk for stress fracture. The project hypothesis in this application is practitioners can diagnose stress fracture
risk prior to injury by detecting abnormal tibial loading and bone microdamage before injury development. The
rationale for this work is that enabling early and accurate diagnosis of tibial stress fracture may be key for
efficacious prevention and treatment modalities, and a substantial reduction in the incidence of this destructive
musculoskeletal injury. The project hypothesis will be tested by pursuing three specific aims: (1) Quantify tibial
bone loads across a range of physical activities; (2) Develop statistical model of tibial loading during physical
activity; and (3) Automate ultrasound use to detect tibial stress fracture. For the first and second aims, we will
collect biomechanical data to evaluate tibial loading during conditions commonly encountered during outdoor
physical activity for individuals with and without history of tibial stress fracture, and mechanically load a tibia to
develop a statistical model of bone loading experienced during single and repeated bouts of physical activity.
For the third aim, we will collect ultrasound images of a tibia shortly after stress fracture and after fracture
symptoms have subsided to standardize image acquisition and analysis techniques to automate detection of
injury. The proposed research is innovative, in the applicant’s opinion, because it seeks to expand foundational
knowledge regarding tibial stress fracture development that can be implemented to facilitate accurate
identification of individuals at risk for stress fracture and enable early detection of the tibial damage that is a
precursor to injury. The proposed research is significant because it will provide the wider scientific community
the valuable knowledge to immediately improve tibial stress fracture diagnosis and treatment, as well as a
strong scientific foundation to develop effective prevention and rehabilitative strategies for this common
musculoskeletal injury. Collectively, these tangible benefits have potential to substantially reduce the
prevalence of this common overuse injury.

## Key facts

- **NIH application ID:** 10557619
- **Project number:** 1P20GM148321-01
- **Recipient organization:** BOISE STATE UNIVERSITY
- **Principal Investigator:** Tyler Brown
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $203,323
- **Award type:** 1
- **Project period:** 2023-04-06 → 2028-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10557619

## Citation

> US National Institutes of Health, RePORTER application 10557619, Emerging Technologies for Early Detection of Distal Leg Stress Fracture. (1P20GM148321-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10557619. Licensed CC0.

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