[HTML][HTML] Introduction to radiomics for a clinical audience
C McCague, S Ramlee, M Reinius, I Selby, D Hulse… - Clinical Radiology, 2023 - Elsevier
Radiomics is a rapidly developing field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …
features from medical images, thus converting these digital images into minable, high …
The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis
J Bian, X Wang, W Hao, G Zhang… - Frontiers in Aging …, 2023 - frontiersin.org
Background In recent years, radiomics has been increasingly utilized for the differential
diagnosis of Parkinson's disease (PD). However, the application of radiomics in PD …
diagnosis of Parkinson's disease (PD). However, the application of radiomics in PD …
[HTML][HTML] A comprehensive framework for parkinson's disease diagnosis using explainable artificial intelligence empowered machine learning techniques
S Priyadharshini, K Ramkumar… - Alexandria Engineering …, 2024 - Elsevier
Parkinson's disease (PD) is the second most prevalent neurological disorder, predominantly
affecting older people. With no existing cure, the early detection of PD, where symptoms are …
affecting older people. With no existing cure, the early detection of PD, where symptoms are …
Using 3D CNN for classification of Parkinson's disease from resting-state fMRI data
Parkinson's disease is a chronic and progressive movement disorder caused by the
degeneration of dopamine-producing neurons in the substantia nigra of the brain. Currently …
degeneration of dopamine-producing neurons in the substantia nigra of the brain. Currently …
Combined brain network topological metrics with machine learning algorithms to identify essential tremor
Q Li, L Tao, P Xiao, H Gui, B Xu, X Zhang… - Frontiers in …, 2022 - frontiersin.org
Background and objective Essential tremor (ET) is a common movement syndrome, and the
pathogenesis mechanisms, especially the brain network topological changes in ET are still …
pathogenesis mechanisms, especially the brain network topological changes in ET are still …
Gray matter atrophy and white matter lesions burden in delayed cognitive decline following carbon monoxide poisoning
Y Zhang, T Wang, S Wang, X Zhuang, J Li… - Human Brain …, 2024 - Wiley Online Library
Gray matter (GM) atrophy and white matter (WM) lesions may contribute to cognitive decline
in patients with delayed neurological sequelae (DNS) after carbon monoxide (CO) …
in patients with delayed neurological sequelae (DNS) after carbon monoxide (CO) …
An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson's disease
H Chen, X Liu, X Luo, J Fu, K Zhou, N Wang… - Frontiers in Aging …, 2024 - frontiersin.org
Objectives The altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable
biomarker in the detection of early-stage Parkinson's disease (EPD). Diagnosis via visual …
biomarker in the detection of early-stage Parkinson's disease (EPD). Diagnosis via visual …
Distinguishing patients with MRI-negative temporal lobe epilepsy from normal controls based on individual morphological brain network
W Zhang, Y Duan, L Qi, Z Li, J Ren, N Nangale… - Brain Topography, 2023 - Springer
Abstract Temporal Lobe Epilepsy (TLE) is the most common subtype of focal epilepsy and
the most refractory to drug treatment. Roughly 30% of patients do not have easily identifiable …
the most refractory to drug treatment. Roughly 30% of patients do not have easily identifiable …
[HTML][HTML] Altered effective connectivity from cerebellum to motor cortex in chronic low back pain: A multivariate pattern analysis and spectral dynamic causal modeling …
Y Chen, Y Yang, Z Gong, Y Kang, Y Zhang… - Brain Research …, 2023 - Elsevier
To explore the central processing mechanism of pain perception in chronic low back pain
(cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional …
(cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional …
PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI
G Li, Y Song, M Liang, J Yu, R Zhai - Journal of Neural …, 2024 - iopscience.iop.org
Objective. The clinical diagnosis of Parkinson's disease (PD) relying on medical history,
clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) …
clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) …