Efficient brain strain estimation is critical for routine application of a head
injury model. Lately, a convolutional neural network (CNN) has been successfully
developed to estimate spatially detailed brain strains instantly and accurately
in contact sports. Here, we extend its application to automotive head impacts,
where impact profiles are typically more complex with longer durations. Head
impact kinematics (N=458) from two public databases were used to generate
augmented impacts (N=2694). They were simulated using the anisotropic Worcester
Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum
principal strain (MPS). For each augmented impact, rotational velocity
(vrot) and the corresponding rotational
acceleration (arot) profiles were concatenated as
static images to serve as CNN input. Three training strategies were evaluated:
1) “baseline”, using random initial weights; 2) “transfer learning”, using
weight transfer from a previous CNN model trained on head impacts drawn from
contact sports; and 3) “combined training”, combining previous training data
from contact sports (N=5661) for training. The combined training achieved the
best performances. For peak MPS, the CNN achieved a coefficient of determination
(R2) of 0.932 and root mean squared error (RMSE)
of 0.031 for the real-world testing dataset. It also achieved a success rate of
60.5% and 94.8% for elementwise MPS, where the linear regression slope,
k, and correlation coefficient, r, between
estimated and simulated MPS did not deviate from 1.0 (when identical) by more
than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the
CNN estimation was also highly accurate compared to those from direct simulation
across a range of thresholds (R2 of 0.899-0.943 with
RMSE of 0.054-0.069). Finally, the CNN achieved an average k
and r of 0.98±0.12 and 0.90±0.07, respectively, for six
reconstructed car crash impacts drawn from two other sources independent of the
training dataset. Importantly, the CNN is able to efficiently estimate
elementwise MPS with sufficient accuracy while conventional kinematic injury
metrics cannot. Therefore, the CNN has the potential to supersede current
kinematic injury metrics that can only approximate a global peak MPS or CSDM.
The CNN technique developed here may offer enhanced utility in the design and
development of head protective countermeasures, including in the automotive
industry. This is the first study aimed at instantly estimating spatially
detailed brain strains for automotive head impacts, which employs >8.8
thousand impact simulations generated from ~1.5 years of nonstop computations on
a high-performance computing platform.