Pioneered the use of deep learning (“AI”) in brain injury modeling

Dr. Ji’s group pioneered the use of deep learning into brain injury impact modeling. The technique transcends the earlier pre-computation method also developed in the lab. It has the potential to shifting future brain injury biomechanical studies from focusing on impact accelerations to tissue strain-based investigation and analysis.

[5] N. Lin, S. Wu, S. Ji, A Morphologically Individualized Deep Learning Brain Injury Model, J. Neurotrauma. 40 (2023) 2233–2247. https://doi.org/10.1089/neu.2022.0413.

[4] S. Wu, W. Zhao, S. Ji, Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact, Comput. Methods Appl. Mech. Eng. 394 (2022) 114913. https://doi.org/10.1016/J.CMA.2022.114913.

[3] K. Ghazi, S. Wu, W. Zhao, S. Ji, Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact, J. Neurotrauma. 38 (2021) 1023–1035. https://doi.org/10.1089/neu.2020.7281.

[2] S. Wu, W. Zhao, S. Barbat, J. Ruan, S. Ji, Instantaneous brain strain estimation for automotive head impacts via deep learning, Stapp Car Crash J. 65 (2021) 139–162.

[1] S. Wu, W. Zhao, K. Ghazi, S. Ji, Convolutional neural network for efficient estimation of regional brain strains, Sci. Rep. 9:17326 (2019). https://doi.org/https://doi.org/10.1038/s41598-019-53551-1.

Largely based on these studies, the TBI biomechanics field has formed consensus to further explore and apply advanced data science techniques, including deep learning (i.e., “AI”), into future brain injury studies. Dr. Ji co-led the work:

S. Ji, M. Ghajari, H. Mao, H. Kraft, Reuben, M. Hajiaghamemar, M.B. Panzer, R. Willinger, M.D. Gilchrist, S. Kleiven, J.D. Stitzel, Use of brain biomechanical models for monitoring impact exposure in contact sports, Ann. Biomed. Eng. 50 (2022) 1389–1408. https://doi.org/10.1007/s10439-022-02999-w.

Comments are closed.