# Leveraging Artificial-Intelligence to Profile and Enhance Phenotypic Plasticity for Second Injury Prevention: An Innovative Precision Medicine Platform to Revolutionize Injury Care

> **NIH NIH R21** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $312,918

## Abstract

Project Summary/Abstract
The best predictor of future injury is previous injury. Unfortunately, this has not changed in a quarter century,
despite the introduction of evidence-based medicine and the associated revisions to post-injury treatment and
care. Nearly nine million sports-related injuries occur annually, and the majority of these require medical
treatment and/or clinical intervention prior to clearance for the athlete to return to play (RTP). Even with formal
care, injured athletes are two- to four times (in extreme cases 100 times) more likely to suffer a second injury for
up to two years after RTP. This is even more alarming given that 65% of initial sports-related injuries affect
children and young adults (ages 5-24 years), setting them up for a lifetime of negative health outcomes. These
data implicate the initial injury event as the tipping point for a post-injury cascade of negative sequelae exposing
athletes to more physical and psychological pain, higher medical costs and greater risk of severe long-term
negative health throughout their life. Thus, an urgent need exists to revolutionize the current standard of post-
injury care through better identification and targeting of deficits that underlie second injury risk to enable everyone
to stay physically active and healthy for their whole lives. To address this need, we propose a pioneering
approach that capitalizes on the biological concept of Phenotypic Plasticity (PP) to quantify an athlete’s functional
adaptability across different performance environments. Here PP is computed based on both intrinsic and
extrinsic factors that reflect the functional movement capability of the athlete. The overall objective of this
proposal is, therefore, to develop a generalizable PP-based precision sports medicine approach for second injury
prevention through the development of a systematic bioinformatics-driven approach for PP profile construction,
and the training of genetic fuzzy artificial intelligence to establish treatment type and magnitude for precision
enhancement of PP. The result of the proposed work will be a generalizable PP-based precision sports medicine
approach via two expected outcomes: (1) a formalized phenotypic precision medicine approach to objectively
quantify behavioral plasticity, and (2) the successful implementation of a platform leveraging genetic fuzzy
artificial intelligence integrated with mixed reality for performance prediction. This platform will support delivery
of personalized treatments for PP enhancement. These objectives are the critical first step for the development
of a generalizable precision medicine approach that will revolutionize rehabilitation and finally stem the tide of
second injury in sport. This contribution will be significant to push back against the initial injury tipping point and,
in doing so, create a pathway for injured athletes to return safely to physical activity and to maintain a long-term
healthy lifestyle.

## Key facts

- **NIH application ID:** 9989850
- **Project number:** 5R21EB027865-02
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Adam Kiefer
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $312,918
- **Award type:** 5
- **Project period:** 2019-08-06 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989850

## Citation

> US National Institutes of Health, RePORTER application 9989850, Leveraging Artificial-Intelligence to Profile and Enhance Phenotypic Plasticity for Second Injury Prevention: An Innovative Precision Medicine Platform to Revolutionize Injury Care (5R21EB027865-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9989850. Licensed CC0.

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