Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation

• Published in: ArXiv.org 2023-06
• Main Authors: Zhan, Xianghao, Sun, Jiawei, Liu, Yuzhe, Cecchi, Nicholas J, Flao, Enora Le, Gevaert, Olivier, Zeineh, Michael M, Camarillo, David B
• Format: Article
• Language: English
• ISSN: 2331-8422

Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose brain deformation estimators that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on on-field head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation accuracy, with the DRCA method significantly outperforming other domain adaptation methods in prediction accuracy (p