Results 261 to 270 of about 3,188,697 (327)

Functional connectivity in EEG: a multiclass classification approach for disorders of consciousness. [PDF]

Front Neurosci
Raveendran S, S K, A G R, Kenchaiah R, Sahoo J, Kumar S, M K F, Mundlamuri RC, Bansal S, V S B, R S.   +10 more
europepmc   +1 more source

Multi‐Risk‐Level Sarcopenia‐Prone Screening via Machine Learning Classification of Sit‐to‐Stand Motion Metrics from Wearable Sensors

Advanced Intelligent Systems, EarlyView.
A machine learning approach utilizes micro inertial measurement units (μIMUs) for noninvasive sarcopenia screening through a sit‐to‐stand test. With 53 older participants wearing IMUs, 510 features are extracted across four motion phases. Five algorithms (support vector machine, K‐nearest neighbors, decision tree, linear discriminant analysis, and ...
Keer Wang, Hongyu Zhang, Clio Yuen Man Cheng, Meng Chen, King Wai Chiu Lai, Calvin Kalun Or, Yong Hu, Arul Lenus Roy Vellaisamy, Cindy Lo Kuen Lam, Ning Xi, Vivian Weiqun Lou, Wen Jung Li   +11 more
wiley   +1 more source

Deep-learning network for automated evaluation of root-canal filling radiographic quality. [PDF]

Eur J Med Res
Jin L, Du B, Xu Z, Bai H, Ding P, Zhang Z, Pan Y, Lin Y, Li Z, Rausch-Fan X, Hu F, Zhang X.   +11 more
europepmc   +1 more source

Deep‐Learning Model for Central Nervous System Infection Diagnosis and Prognosis Using Label‐Free 3D Immune‐Cell Morphology in the Cerebrospinal Fluid

Advanced Intelligent Systems, EarlyView.
Early diagnosis and prognosis prediction of central nervous system infections are critical. This study introduces a deep‐learning model utilizing cerebrospinal fluid immune‐cell morphology captured via 3D holotomography. The model effectively predicts infection etiology and prognosis, achieving high accuracy.
Bo Kyu Choi, Ho Heon Yang, Jong Hyun Kim, JaeSeong Hong, Kyung Min Kim, Yu Rang Park   +5 more
wiley   +1 more source

In-Hospital Mortality Prediction among Intensive Care Unit Patients with Acute Ischemic Stroke: A Machine Learning Approach. [PDF]

Health Data Sci
Cummins JA, Gerber BS, Fukunaga MI, Henninger N, Kiefe CI, Liu F.   +5 more
europepmc   +1 more source

Artificial Intelligence‐Driven Robotic Sensing System for Noninvasive Crop Health Monitoring and Autonomous Irrigation Management

Advanced Intelligent Systems, EarlyView.
A mobile robot equipped with a 3D‐printed, noninvasive electrophysiological sensor for real‐time monitoring of tomato plant health based on irrigation levels is presented. The sensor provides high electrical reliability, sensitive electrophysiological signal detection (0.0122 mV), and 86.91% classification accuracy using scalogram images, thereby ...
Yiting Chen, Yan Sum Yip, Hieu T. Tran, Soomin Shin, Woo Soo Kim   +4 more
wiley   +1 more source

A retrospective study using machine learning to develop predictive model to identify rotavirus-associated acute gastroenteritis in children. [PDF]

PeerJ
Paul S, Rahman M, Dolley A, Saikia K, Shyamsunder Singh C, Mohammed A, Muteeb G, Sarmah R, Namsa ND.   +8 more
europepmc   +1 more source

Compound Heterozygous Variants in ZSWIM7 Gene Linked to Infertility and Its Role in Gonadal Development

American Journal of Medical Genetics Part A, EarlyView.
ABSTRACT This study aims to elucidate the role of the ZSWIM7 gene in the etiology of infertility, focusing on its potential association with premature ovarian insufficiency and azoospermia. We investigated an 18‐year‐old female patient who presented with primary amenorrhea, hypoplasia of the uterus and ovaries, Tanner stage II breast and pubic hair ...
Denise M. Christofolini, Guilherme Pinn, Thainá Vilella, Maria I. Melaragno, Mariana Moysés‐Oliveira, Luciano Pompei, Rare Genomes Project Consortium, Bianca Bianco, Caio P. Barbosa   +8 more
wiley   +1 more source

A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets. [PDF]

Sci Rep
Nasir MU, Naseem MT, Ghazal TM, Zubair M, Ali O, Abbas S, Ahmad M, Adnan KM.   +7 more
europepmc   +1 more source

GABAergic modulation of beta power enhances motor adaptation in frontotemporal lobar degeneration

Alzheimer's &Dementia, EarlyView.
Abstract INTRODUCTION We examined how abnormal prefrontal neurophysiology and changes in gamma‐aminobutyric acid‐ergic (GABAergic) neurotransmission contribute to behavioral impairments in disorders associated with frontotemporal lobar degeneration (FTLD). METHODS We recorded magnetoencephalography during an adaptive visuomotor task from 11 people with
Laura E. Hughes, Natalie E. Adams, Matthew A. Rouse, Michelle Naessens, Alexander Shaw, Alexander G. Murley, Thomas E. Cope, Negin Holland, David Nesbitt, Duncan Street, David J. Whiteside, James B. Rowe   +11 more
wiley   +1 more source