4.7 Article

Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using a deep learning model

Related references

Note: Only part of the references are listed.
Article Clinical Neurology

A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

Caglar Uyulan et al.

Summary: Automatic detection of ADHD based on fMRI using Deep Learning addresses the curse of-dimensionality problem and provides a robust solution. A transfer learning approach using ResNet-50 CNN achieved a classification accuracy of 93.45%, and Class Activation Map analysis revealed differences in brain regions between ADHD and healthy children.

CLINICAL EEG AND NEUROSCIENCE (2023)

Article Clinical Neurology

Differentiating Multiple Sclerosis From AQP4-Neuromyelitis Optica Spectrum Disorder and MOG-Antibody Disease With Imaging

Rosa Cortese et al.

Summary: This study investigated whether imaging characteristics could differentiate between relapsing-remitting multiple sclerosis (RRMS), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4-NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). The results showed that the proportion of lesions with the central vein sign (CVS) was the most accurate measure to differentiate RRMS from AQP4-NMOSD, while white matter lesions were the most accurate measure to discriminate RRMS from MOGAD.

NEUROLOGY (2023)

Article Multidisciplinary Sciences

Association of subcortical structural shapes with fatigue in neuromyelitis optica spectrum disorder

Jin Myoung Seok et al.

Summary: This study explored the relationship between fatigue and subcortical structures in patients with neuromyelitis optica spectrum disorder (NMOSD). The results showed that atrophy in the right thalamus is strongly correlated with fatigue severity, indicating that the local shape volume of the right thalamus may serve as a biomarker of fatigue in NMOSD.

SCIENTIFIC REPORTS (2022)

Review Clinical Neurology

Artificial intelligence as an emerging technology in the current care of neurological disorders

Urvish K. Patel et al.

Summary: This paper aims to guide medical practitioners on aspects of artificial intelligence in neurology, focusing on unsupervised machine learning applications to improve patient outcomes. Various forms of AI, outcomes, benefits, limitations, and future directions are mentioned, with experimental examples of AI utilization in neurology discussed.

JOURNAL OF NEUROLOGY (2021)

Review Clinical Neurology

Detection of MOG-IgG by cell-based assay: moving from discovery to clinical practice

Amanda Marchionatti et al.

Summary: Myelin oligodendrocyte glycoprotein (MOG) is a unique CNS-specific mammalian protein that is targeted by autoantibodies, aiding in distinguishing a subgroup of patients and reducing the risk of clinical misdiagnosis. The development of cell-based assays (CBA) has improved the detection of clinically meaningful MOG-IgG binding.

NEUROLOGICAL SCIENCES (2021)

Article Biochemical Research Methods

Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging

Yunyan Zhang et al.

Summary: This study investigated three heatmap-generating techniques for CNN interpretation, with Grad-CAM showing the best ability in heatmap localization. Through training and examining six different models, the impact of CNNs on heatmap generation was demonstrated. By analyzing the performance of the VGG19 model, differences in the brain regions between SPMS and RRMS were identified.

JOURNAL OF NEUROSCIENCE METHODS (2021)

Article Computer Science, Interdisciplinary Applications

TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning

Fernando Perez-Garcia et al.

Summary: TorchIO is an open-source Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. It encourages good open-science practices, supports experiment reproducibility, and is version-controlled for precise citation. The modular library is compatible with other frameworks for deep learning with medical images.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

Article Medicine, General & Internal

Neuromyelitis optica

Sven Jarius et al.

NATURE REVIEWS DISEASE PRIMERS (2020)

Article Clinical Neurology

Brain MRI characteristics in neuromyelitis optica spectrum disorders: A large multi-center retrospective study in China

Guanmei Cao et al.

MULTIPLE SCLEROSIS AND RELATED DISORDERS (2020)

Article Clinical Neurology

Incidence of multiple sclerosis misdiagnosis in referrals to two academic centers

Marwa Kaisey et al.

MULTIPLE SCLEROSIS AND RELATED DISORDERS (2019)

Article Multidisciplinary Sciences

Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies

Tomomichi Iizuka et al.

SCIENTIFIC REPORTS (2019)

Article Clinical Neurology

Brain and cord imaging features in neuromyelitis optica spectrum disorders

Laura Cacciaguerra et al.

ANNALS OF NEUROLOGY (2019)

Article Computer Science, Theory & Methods

A survey on Image Data Augmentation for Deep Learning

Connor Shorten et al.

JOURNAL OF BIG DATA (2019)

Article Clinical Neurology

Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria

Alan J. Thompson et al.

LANCET NEUROLOGY (2018)

Review Clinical Neurology

The current role of MRI in differentiating multiple sclerosis from its imaging mimics

Ruth Geraldes et al.

NATURE REVIEWS NEUROLOGY (2018)

Review Neurosciences

FreeSurfer

Bruce Fischl

NEUROIMAGE (2012)

Article Clinical Neurology

Serologic diagnosis of NMO A multicenter comparison of aquaporin-4-IgG assays

P. J. Waters et al.

NEUROLOGY (2012)

Article Clinical Neurology

Interferon Beta Treatment in Neuromyelitis Optica Increase in Relapses and Aquaporin 4 Antibody Titers

Jacqueline Palace et al.

ARCHIVES OF NEUROLOGY (2010)

Article Computer Science, Artificial Intelligence

The role of image registration in brain mapping

AW Toga et al.

IMAGE AND VISION COMPUTING (2001)