A Morphologically Individualized Deep Learning Brain Injury Model.

Area of Science:

• Computational Biomechanics
• Neuroscience
• Medical Imaging and Simulation

Background:

• Existing brain injury models lack subject specificity and simulation efficiency.
• Individual morphological variations significantly influence brain strain responses.
• The Worcester Head Injury Model (WHIM) V1.0 provides a basis for brain injury modeling.

Purpose of the Study:

• To extend an existing convolutional neural network (CNN) brain model for subject-specific injury prediction.
• To incorporate individual morphological variations using linear scaling factors as CNN inputs.
• To improve the accuracy and efficiency of brain injury simulations.

Main Methods:

• Developed an instantaneous CNN brain model based on the WHIM V1.0.
• Used linear scaling factors relative to the generic WHIM as additional CNN inputs.
• Generated training samples by scaling the WHIM and augmenting head impact data.
• Validated model accuracy using linear regression slope and Pearson's correlation coefficient.

Main Results:

• The individualized CNN achieved an 86.2% success rate in cross-validation for scaled model responses.
• Achieved a 92.1% success rate in independent generic model testing.
• The model accurately estimates subject-specific, spatially detailed peak brain strains without neuroimages.

Conclusions:

• The individualized CNN model enhances subject specificity and simulation efficiency in brain injury modeling.
• This tool is particularly beneficial for predicting injuries in youths and females due to morphological differences.
• Potential applications include injury mitigation and the design of head protective gear, promoting research collaboration.