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Predicting distal tibia fracture type using demographic, vehicle, and crash factors via a random forest
L Garrett Bangert1, William Armstrong2, Edward Shangin1
1School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia.
Traffic Injury Prevention
|July 31, 2025
Summary
This study used machine learning to predict distal tibia fracture types from crash data, identifying key factors like toe-pan intrusion and delta-v. The model achieved accuracy comparable to human experts, aiding in post-crash assessment.
Area of Science:
- Orthopedic trauma research
- Biomechanics of injury
- Machine learning in healthcare
Background:
- Distal tibia fractures are common in vehicle crashes, leading to significant complications.
- Accurate fracture classification is crucial for effective treatment and prognosis.
- Existing classification methods may not fully leverage real-world crash data.
Purpose of the Study:
- To develop a predictive model for distal tibia fracture types using real-world crash data.
- To identify key demographic, vehicle, and crash factors associated with specific fracture types.
- To evaluate the performance of a random forest algorithm in classifying fractures.
Main Methods:
- Utilized Crash Injury Research and Engineering Network (CIREN) data from 2005-2024.
- Classified distal tibia fractures into AO/OTA types (extraarticular, partial articular, complete articular).
- Trained a random forest classifier on crash factors and employed SHapley Additive exPlanations (SHAP) for analysis.
Main Results:
- The random forest model accurately predicted fracture type in 75.5% of cases.
- Increased delta-v (>30 kph) and toe-pan intrusion correlated with complete articular fractures.
- Knee bolster airbag deployment was associated with a decreased probability of partial articular fractures.
Conclusions:
- Machine learning models can predict distal tibia fracture types with accuracy similar to human graders, without using radiology.
- The model effectively identifies critical crash factors influencing fracture patterns.
- This approach offers potential for improved post-crash triage and injury analysis.
Keywords:
CIRENMachine learning classificationclinical injury classificationlower leg injury epidemiologymachine learning interpretabilityMore Related Videos
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