人工智能
计算机科学
机器学习
过度拟合
人工神经网络
领域(数学分析)
模式识别(心理学)
数学
数学分析
作者
Xianghao Zhan,Jun Sun,Yuzhe Liu,Nicholas J. Cecchi,Enora Le Flao,Olivier Gevaert,Michael Zeineh,David B. Camarillo
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2024-03-01
卷期号:24 (5): 7097-7106
标识
DOI:10.1109/jsen.2023.3349213
摘要
Machine learning head models (MLHMs) are developed to estimate brain deformation from sensor-based kinematics for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the decreasing accuracy caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose a new MLHM configuration 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 target 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 outperforming other domain adaptation methods in prediction accuracy: MPS mean absolute error (MAE): 0.017 (CF) and 0.020 (MMA); MPSR MAE: 4.09 s -1 (CF) and 6.61 s -1 (MMA). On another two hold-out test sets with 195 college football impacts and 260 boxing impacts, the DRCA model outperformed the baseline model without domain adaptation in MPS and MPSR estimation MAE. The DRCA domain adaptation approach reduces the error of MPS/MPSR estimation to be well below previously reported TBI thresholds, enabling accurate brain deformation estimation to detect TBI in future clinical applications.
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