no code implementations • 8 Jun 2023 • Xianghao Zhan, Jiawei Sun, Yuzhe Liu, Nicholas J. Cecchi, Enora Le Flao, Olivier Gevaert, Michael M. Zeineh, David B. Camarillo
Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI).
no code implementations • 19 Dec 2022 • Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Ashlyn A. Callan, Enora Le Flao, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body.
no code implementations • 27 Oct 2021 • Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
The brain dynamics decomposition enables better interpretation of the patterns in brain injury metrics and the sensitivity of brain injury metrics across impact types.
no code implementations • 31 Aug 2021 • Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
To address the computational cost of FEM, the limited strain rate prediction, and the generalizability of MLHMs to on-field datasets, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR).
no code implementations • 7 Aug 2021 • Xianghao Zhan, Yiheng Li, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain.
no code implementations • 30 Apr 2021 • Samuel J. Raymond, David B. Camarillo
Physics-Informed Machine Learning (PIML) has gained momentum in the last 5 years with scientists and researchers aiming to utilize the benefits afforded by advances in machine learning, particularly in deep learning.
BIG-bench Machine Learning Physics-informed machine learning
no code implementations • 19 Apr 2021 • Xianghao Zhan, Yiheng Li, Yuzhe Liu, Nicholas J. Cecchi, Samuel J. Raymond, Zhou Zhou, Hossein Vahid Alizadeh, Jesse Ruan, Saeed Barbat, Stephen Tiernan, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e. g., football, car crash, mixed martial arts).
no code implementations • 9 Feb 2021 • Xianghao Zhan, Yiheng Li, Yuzhe Liu, August G. Domel, Hossein Vahid Alizadeh, Zhou Zhou, Nicholas J. Cecchi, Samuel J. Raymond, Stephen Tiernan, Jesse Ruan, Saeed Barbat, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e. g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events.
no code implementations • 18 Dec 2020 • Xianghao Zhan, Yiheng Li, Yuzhe Liu, August G. Domel, Hossein Vahid Alizadeh, Samuel J. Raymond, Jesse Ruan, Saeed Barbat, Stephen Tiernan, Olivier Gevaert, Michael Zeineh, Gerald Grant, David B. Camarillo
The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types.
no code implementations • 16 Oct 2020 • Xianghao Zhan, Yuzhe Liu, Samuel J. Raymond, Hossein Vahid Alizadeh, August G. Domel, Olivier Gevaert, Michael Zeineh, Gerald Grant, David B. Camarillo
Results: The proposed deep learning head model can calculate the maximum principal strain for every element in the entire brain in less than 0. 001s (with an average root mean squared error of 0. 025, and with a standard deviation of 0. 002 over twenty repeats with random data partition and model initialization).
no code implementations • 16 Oct 2020 • August G. Domel, Samuel J. Raymond, Chiara Giordano, Yuzhe Liu, Seyed Abdolmajid Yousefsani, Michael Fanton, Ileana Pirozzi, Ali Kight, Brett Avery, Athanasia Boumis, Tyler Fetters, Simran Jandu, William M Mehring, Sam Monga, Nicole Mouchawar, India Rangel, Eli Rice, Pritha Roy, Sohrab Sami, Heer Singh, Lyndia Wu, Calvin Kuo, Michael Zeineh, Gerald Grant, David B. Camarillo
Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered.
no code implementations • 16 Jul 2019 • Jake Sganga, David Eng, Chauncey Graetzel, David B. Camarillo
To improve intraoperative registration, we develop two deep learning approaches to localize the bronchoscope in the preoperative CT map based on the bronchoscopic video in real-time, called AirwayNet and BifurcationNet.
no code implementations • 25 Mar 2019 • Jake Sganga, David Eng, Chauncey Graetzel, David B. Camarillo
We developed two deep learning approaches to localize the bronchoscope in the preoperative CT map in real time and tested the algorithms across 13 trajectories in a lung phantom and 68 trajectories in 11 human cadaver lungs.