Results 181 to 190 of about 16,616 (239)

AI-driven feature recognition of SEM profiles in deep reactive ion etching based on physics-constrained variational autoencoder. [PDF]

Microsyst Nanoeng
Wang F, Yu H, Miao Y, Sun K, Sun Y, Yang H, Li X.   +6 more
europepmc   +1 more source

Fully automated three‐dimensional deep learning‐based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery

Epilepsia, EarlyView.
Abstract Objective There are several clinical and research applications for determining the amount of brain tissue resected after epilepsy surgery; however, manual segmentation of postoperative magnetic resonance imaging (MRI) is imprecise and time‐consuming.
Raphael Fernandes Casseb, Brunno Machado de Campos, Wallace Souza Loos, Marcelo Eduardo Ramos Barbosa, Marina Koutsodontis Machado Alvim, Gabriel Chagas Lutfala Paulino, Enrico Ghizoni, Francesco Pucci, Samuel Worrell, Clarissa Lin Yassuda, Roberto Medeiros de Souza, Lara Jehi, Fernando Cendes   +12 more
wiley   +1 more source

Power Load Probabilistic Prediction Based on Multi-Value Quantile Regression and Timing Fusion Ensemble Learning Model. [PDF]

Entropy (Basel)
Liu Y, Mei F, Zhang J, Dai X, Li W.
europepmc   +1 more source

Gamma suppression correlates with thalamic stimulation therapeutic response in intractable epilepsy

Epilepsia, EarlyView.
Abstract Objective In patients with drug‐resistant epilepsy who undergo anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS), efficacy is assessed months after therapy initiation and clinicians have no guidance when choosing stimulation parameters due to the lack of real‐time biomarkers.
Zachary T. Sanger, Xinbing Zhang, Thomas Lisko, Hafsa Farooqi, Steffen Ventz, Sandipan Pati, Thomas R. Henry, David P. Darrow, Michael C. Park, Theoden I. Netoff, Robert A. McGovern   +10 more
wiley   +1 more source

Integrated photonic 3D tensor processing engine. [PDF]

Light Sci Appl
Wu Y, Ni Z, Li X, Wang Y, Lu L, Chen J, Zhou L.   +6 more
europepmc   +1 more source

Programming of deep brain stimulation of the centromedian nucleus of the thalamus for drug‐resistant epilepsy: A meta‐analysis and proposed programming framework

Epilepsia, EarlyView.
Abstract Objective Although the centromedian nucleus of the thalamus (CM) is an increasingly considered deep brain stimulation (DBS) target for drug‐resistant epilepsy (DRE), there is significant variability in programming practices, which may contribute to heterogenous outcomes.
Mohammed A. AlQahtani, Michal Stern‐Zimmer, Puneet Jain, Suvasini Sharma, Alfonso Fasano, Alexander Rotenberg, George M. Ibrahim, Carolina Gorodetsky   +7 more
wiley   +1 more source

Automated cone photoreceptor detection using synthetic data and deep learning in confocal adaptive optics scanning laser ophthalmoscope images. [PDF]

Sci Rep
Shah M, Young LK, Downes SM, Smithson HE, Namburete AIL.   +4 more
europepmc   +1 more source

Nucleus basalis functional connectivity aberrations in temporal lobe epilepsy and improvements after successful epilepsy surgery

Epilepsia, EarlyView.
Abstract Objective Resective surgery achieves seizure freedom in approximately 60%–80% of patients with drug‐resistant temporal lobe epilepsy (TLE), yet the extent to which seizure cessation permits recovery of disrupted functional brain networks remains unclear.
Addison C. Cavender, Derek J. Doss, Ghassan S. Makhoul, Bruno Hidalgo Monroy Lerma, Anas Reda, C. Price Withers, Emily Liao, Kate Wang, Hernán F. J. González, Sarah E. Goodale, Graham W. Johnson, Laurie E. Cutting, Sarah K. Bick, Catie Chang, Victoria L. Morgan, Dario J. Englot   +15 more
wiley   +1 more source

Parameter Adaptive Network for Large-Scale Neural In-Loop Filtering in Versatile Video Coding. [PDF]

Sensors (Basel)
Wu Y, Cai F, Song X, Zuo X.
europepmc   +1 more source

Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen, Christian Málaga‐Chuquitaype   +1 more
wiley   +1 more source