From the College Athletics edition of October 4, 2026
East Texas A&M abstract: DXA and DARI screening ML predicts 180-day injury risk in 1,258 NCAA DI athlete records with AUC 0.74
East Texas A&M University ROARHP Lab (Strogalev, Bernhardt, Oldham et al.) · NCAA Division I sports science / athlete health · United States (Commerce, Texas)
- Publisher
- International Journal of Exercise Science: Conference Proceedings (TACSM abstracts)
- Original publication
- 2026 (Texas ACSM abstract, Int J Exerc Sci Conference Proceedings Vol 2 Iss 18; exact date not stated)
- Source retrieved
- 2026-10-05
What happened
A retrospective two-stage CatBoost model using DXA body composition, DARI markerless motion analysis, and Y-Balance data from 1,258 NCAA DI athlete records (246 injuries within 180 days; scans Aug 2022 to Dec 2025) reached AUC 0.74 on a hold-out set for any injury. Body-region prediction among injured athletes had 50.0% top-1, 62.5% top-2, and 77.1% top-3 accuracy. The authors note single-site prediction remains challenging given class imbalance and limited samples.
Why it matters
Why now: Ohio State announced Project SCARLET (Sep 29 to Oct 1, 2026), using predictive models across 36 sports; this abstract gives a concrete, modest benchmark for what screening-based injury prediction achieves in Division I data.
Evidence and measured results
Master’s-category conference abstract; retrospective single-institution dataset; hold-out testing but no external validation, calibration, or prospective test reported. AUC 0.74 for binary injury risk; body-region top-k accuracies as stated. No sport-by-sport breakdown or intervention outcome.
Limitations and uncertainty
Conference abstract, not peer-reviewed full paper; single institution; retrospective; publication date not stated beyond 2026; AUC 0.74 is moderate discrimination.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-04; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Programs collect screening data but lack evidence on how well it predicts injuries.
- Stakeholders
- Head team physician
- Director of sports medicine
- Strength and conditioning director
- Sports science staff
- Compliance/privacy officer
- Discovery questions
- Do you link DXA and motion screening to injury records?
- What predictive performance would justify clinical use?
- Who decides on interventions from a risk score?
- What athlete consent covers this use?
- Value hypothesis
- Local validation of screening-based models can show whether risk scores add useful information to clinicians.
- Potential engagement
- A retrospective validation study on the department’s own screening and injury data.
- Unsupported claims
- Injury prevention or reduced injury rates
- Accurate single-site injury prediction
- Generalization beyond one institution
- Clinical-grade decision support
Pre-sales engineering
Role takeaway
- Fit
- Programs already running DXA and DARI or similar screening with structured injury records.
- Architecture and integration
- ETL from DXA, DARI, and balance tests plus injury database into a model pipeline with hold-out evaluation.
- Prerequisites
- Consistent athlete IDs across systems
- Defined injury taxonomy and 180-day window
- Data-sharing agreement with sports medicine
- Constraints
- Class imbalance
- Single-site data
- Changes in screening protocol over time
- Security
- Treat all inputs as health data; role-based access; de-identify for model development.
- Proof of value
- Time-based hold-out AUC and calibration
- Subgroup performance by sport and sex
- Prospective one-season test
Delivery
Role takeaway
- Work
- Pilot an injury-risk model under sports-medicine governance with prospective tracking.
- Dependencies
- Medical and performance data integration
- Consent updates
- Clinical review process
- Ownership
- Sports medicine leadership, with sports science support.
- Skills and adoption
- Train clinicians and coaches on interpreting moderate-accuracy risk scores.
- Governance checkpoints
- IRB or privacy review
- Threshold and action-plan approval
- End-of-season calibration review
- Acceptance criteria
- Proposed: prospective AUC and calibration reported after one season
- Proposed: documented clinician review of every flagged athlete
- Risks
- Over-trusting moderate predictions
- Misuse in playing-time or roster decisions
- Privacy breaches of health data
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Combines DXA, DARI motion capture, and balance testing into a model pipeline; requires linked injury records from sports medicine. Interpretation: data integration across performance and medical systems is the main build effort.
Governance
Who approves, reviews and stays accountable for outcomes?
Injury-risk scores touch medical decisions; departments should keep them as decision support under sports-medicine authority with documented thresholds and athlete communication.
Security and privacy
What data, permissions and controls need testing?
DXA scans, motion data, and injury records are sensitive health information; linking them requires consent, access controls, and retention rules.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Requires sports-science analytic capacity; clinicians need training on probabilistic outputs.
Procurement
What should contracts, pricing and exit terms secure?
Vendors offering injury prediction should provide external validation and calibration; buyers should require local validation rights.
Operating model
Which teams own the service once it runs?
Bridges performance and sports medicine data; governance must keep medical authority over any intervention.
Publication history
- 2026-10-04College Athletics · Issue 284 resources
Stable resource ID: etamu-ncaa-di-dxa-dari-injury-risk-ml-2026