Zhan Xianghao, Sun Jiawei, Liu Yuzhe, Cecchi Nicholas J, Le Flao Enora, Gevaert Olivier, Zeineh Michael M, Camarillo David B
Department of Bioengineering, Stanford University, CA, 94305, USA.
School of Biological Science and Medical Engineering, BeiHang University, Beijing, 10019, China.
ArXiv. 2023 Jun 8:arXiv:2306.05255v1.
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 MPS RMSE: 0.027 (CF) and 0.037 (MMA); MPSR RMSE: 7.159 (CF) and 13.022 (MMA). On another two hold-out testsets with 195 college football impacts and 260 boxing impacts, the DRCA model significantly outperformed the baseline model without domain adaptation in MPS and MPSR estimation accuracy . The DRCA domain adaptation reduces the MPS/MPSR estimation error to be well below TBI thresholds, enabling accurate brain deformation estimation to detect TBI in future clinical applications.
机器学习头部模型(MLHMs)的开发旨在估计脑变形,以早期检测创伤性脑损伤(TBI)。然而,当前MLHMs对模拟撞击的过拟合以及不同头部撞击数据集分布变化导致的缺乏通用性,阻碍了其在临床中的广泛应用。我们提出了一种脑变形估计器,它将无监督域适应与深度神经网络相结合,以预测全脑最大主应变(MPS)和MPS率(MPSR)。利用12780次模拟头部撞击,我们使用域正则化成分分析(DRCA)和基于循环生成对抗网络(cycle-GAN)的方法,对来自302次大学橄榄球(CF)撞击和457次综合格斗(MMA)撞击的现场头部撞击进行了无监督域适应。新模型提高了MPS/MPSR估计精度,DRCA方法在预测精度方面显著优于其他域适应方法(MPS均方根误差:CF为0.027,MMA为0.037;MPSR均方根误差:CF为7.159,MMA为13.022)。在另外两个包含195次大学橄榄球撞击和260次拳击撞击的保留测试集上,DRCA模型在MPS和MPSR估计精度方面显著优于未进行域适应的基线模型。DRCA域适应将MPS/MPSR估计误差降低至远低于TBI阈值,从而能够在未来临床应用中进行准确的脑变形估计以检测TBI。