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Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls

SEMINARS IN OPHTHALMOLOGY (2023)

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Article Computer Science, Information Systems

A privacy preserving framework for federated learning in smart healthcare systems

Wenshuo Wang et al.

Summary: FRESH is a comprehensive smart healthcare framework based on FL and ring signature defense. It collects physiological data from individuals using wearable devices, trains ML models locally, and uploads model parameters to the central server for joint training. By utilizing ring signature to hide the source of parameter updates, it effectively resists source inference attacks.

INFORMATION PROCESSING & MANAGEMENT (2023)

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Review Clinical Neurology

Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective

Massimo Filippi et al.

Summary: The use of MRI in the diagnosis of MS has evolved considerably, with the introduction of the 2017 McDonald criteria and new MRI markers. Artificial intelligence tools may complement human assessment in improving diagnosis and patient classification.

JOURNAL OF NEUROLOGY (2023)

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Article Environmental Sciences

A Local-Global Framework for Semantic Segmentation of Multisource Remote Sensing Images

Luyi Qiu et al.

Summary: In this paper, a novel Local-global Framework is proposed, which efficiently utilizes features extracted from multiple sources and fully captures features of local and global regions. The framework consists of a dual-source fusion network and local-global transformer modules. The dual-source fusion network selectively fuses features from multiple sources and reduces the interference of redundant features. The local-global transformer module captures fine-grained local features and coarse-grained global features, enabling the framework to focus on recognizing multiple-scale objects.

REMOTE SENSING (2023)

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Review Automation & Control Systems

Artificial Intelligence in Healthcare: Review, Ethics, Trust Challenges & Future Research Directions

Pranjal Kumar et al.

Summary: The use of artificial intelligence in medicine is revolutionizing current procedures in healthcare. However, there are concerns about trust, ethics, and integration of this technology. This paper aims to provide researchers with a comprehensive understanding of AI and its medical applications, as well as the challenges associated with its large-scale integration.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2023)

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Review Clinical Neurology

Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis

Fardin Nabizadeh et al.

Summary: This study conducted a systematic review and meta-analysis on the role of artificial intelligence (AI) in the diagnosis of multiple sclerosis (MS). The results showed that the use of AI models can improve diagnostic accuracy in MS patients and enhance current diagnostic approaches.

NEUROLOGICAL SCIENCES (2023)

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Article Chemistry, Analytical

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images

Esraa Hassan et al.

Summary: Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. In this paper, an enhanced OCT model based on modified ResNet (50) and random forest algorithms is proposed to classify retinal OCT. The experimentation results demonstrate high performance in sensitivity, specificity, and precision.

SENSORS (2023)

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Article Clinical Neurology

Diagnosis of multiple sclerosis using optical coherence tomography supported by artificial intelligence

Miguel Ortiz et al.

Summary: This study aimed to identify new biomarkers for the early diagnosis of multiple sclerosis (MS) using spectral-domain optical coherence tomography (OCT) and artificial intelligence. The analysis of retinal thickness and inter-eye difference revealed that the greatest alteration occurred in the ganglion cell, inner plexiform, and inner retinal layers. By using these parameters for automatic diagnosis, an accuracy of 87%, sensitivity of 82%, and specificity of 92% were achieved, suggesting that neuroretinal structure analysis could be incorporated into the diagnostic criteria for MS.

MULTIPLE SCLEROSIS AND RELATED DISORDERS (2023)

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Article Clinical Neurology

Discrimination of multiple sclerosis using OCT images from two different centers

Zahra Khodabandeh et al.

Summary: This study investigates the use of OCT for retinal imaging in monitoring multiple sclerosis (MS). The authors employ multilayer segmented OCTs to accurately discriminate between MS and healthy controls (HCs). The proposed algorithm provides interpretability and shows promising results in classifying MS and HCs.

MULTIPLE SCLEROSIS AND RELATED DISORDERS (2023)

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Article Medicine, General & Internal

Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank

Rachel Marjorie Wei Wen Tseng et al.

Summary: This study aims to validate a novel cardiovascular disease risk stratification system (Reti-CVD) based on retinal photographs. The results show that Reti-CVD accurately predicts cardiovascular events and helps discern the borderline risk group. This study is of great significance for early intervention in cardiovascular disease.

BMC MEDICINE (2023)

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Review Construction & Building Technology

A Review: How Deep Learning Technology Impacts the Evaluation of Traditional Village Landscapes

Tao Wang et al.

Summary: Recently, the utilization of deep learning technology in the study of traditional village landscape has gained attention. It is primarily used to explore the representation and diversity of cultural heritage information. The study comprehensively reviewed deep learning-related literature, specifically focusing on landscape image recognition using pixel-level semantic segmentation algorithm and image feature extraction technology. The objective was to make cultural heritage digitally and visually accessible for user-centred exploration. Additionally, the study attempted to identify the physical attributes and spatial characteristics of traditional village landscapes and simulate the value perception of experts and the public, offering a scientific basis and technical support for the protection and utilization of traditional villages.

BUILDINGS (2023)

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Article Multidisciplinary Sciences

Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomography

Monica Hernandez et al.

Summary: This study investigates the anterior visual pathway in Multiple Sclerosis (MS) patients, aiming to find the most discriminative features for the computer-aided diagnosis of MS using optical coherence tomography. The study compares two black-box methods (gradient boosting and random forests) with a glass-box method (explainable boosting machine), and finds that the glass-box method performs the best in terms of explainability and accuracy. The study provides important insights into developing a trustworthy computer-aided solution for the diagnosis of MS with OCT.

PLOS ONE (2023)

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Article

Convolutional Neural Networks: A Survey

Moez Krichen

Computers (2023)

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Article Chemistry, Analytical

Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation

Almudena Lopez-Dorado et al.

Summary: This paper aims to use a convolutional neural network to assist in the early diagnosis of multiple sclerosis by classifying images from swept-source optical coherence tomography. The study identifies the retinal structures with the highest discriminant capacity and achieves high sensitivity and specificity through thresholding these images and using them as inputs to the network.

SENSORS (2022)

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Article Engineering, Biomedical

Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis

Alberto Montolio et al.

Summary: Machine learning approaches using optical coherence tomography (OCT) for measuring retinal nerve fiber layer (RNFL) thickness can be used for the diagnosis and prognosis of multiple sclerosis (MS). The study found that the best acquisition protocol for MS diagnosis was the fast macular thickness protocol, achieving high accuracy, sensitivity, specificity, precision, and AUC. The measurements of RNFL thickness obtained with Spectralis OCT were also shown to have predictive value for disability progression in MS patients.

ANNALS OF BIOMEDICAL ENGINEERING (2022)

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Article Biology

Modular deep neural networks for automatic quality control of retinal optical coherence tomography scans

Josef Kauer-Bonin et al.

Summary: In this study, a standardized, real-time feedback system for automatic quality assessment of retinal OCT images was proposed and validated. Modular neural networks were trained to analyze image quality features, generating a comprehensive quality report. The method showed promising results compared to manual grading.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach

Giuseppe Pontillo et al.

Summary: Using unsupervised machine learning, patients with multiple sclerosis (pwMS) were stratified based on brain MRI-derived volumetric features, providing reliable and predictive stratification of pwMS.

EUROPEAN RADIOLOGY (2022)

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Article Multidisciplinary Sciences

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset

Chuizheng Meng et al.

Summary: The recent release of large-scale healthcare datasets has accelerated the research on data-driven deep learning models in healthcare applications. This study focuses on analyzing the interpretability, dataset representation bias, and prediction fairness of deep learning models on the largest publicly available healthcare dataset, MIMIC-IV. The findings reveal that prediction performance is not the sole factor to consider when evaluating healthcare models, as high performance may be achieved through unfair utilization of demographic features. The study suggests that future research should incorporate interpretability and fairness analysis to ensure superior performance without introducing bias.

SCIENTIFIC REPORTS (2022)

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Review Clinical Neurology

Artificial intelligence in the diagnosis of multiple sclerosis: A systematic review

Fardin Nabizadeh et al.

Summary: In the diagnosis of multiple sclerosis (MS), the use of new markers and artificial intelligence (AI) is a rapidly growing field of research, with magnetic resonance imaging (MRI) being the primary modality followed by optical coherence tomography (OCT), serum and cerebrospinal fluid markers, and motor associated markers.

MULTIPLE SCLEROSIS AND RELATED DISORDERS (2022)

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Review Surgery

Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

Nithesh Naik et al.

Summary: The legal and ethical issues of Artificial Intelligence (AI) in healthcare include privacy, surveillance, bias, and the role of human judgment. Currently, there are no well-defined regulations in place to address these concerns, highlighting the need for algorithmic transparency, privacy protection, and cybersecurity measures.

FRONTIERS IN SURGERY (2022)

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Article Medicine, General & Internal

Retinal imaging with optical coherence tomography in multiple sclerosis: novel aspects

Elisabeth Olbert et al.

Summary: Optical coherence tomography (OCT) is gaining attention as a clinical assessment tool in multiple sclerosis (MS) to understand changes in retinal layers during acute optic neuritis and the disease course, which are also associated with clinical outcomes. Further research is needed to determine the significance of implementing OCT parameters in the clinical standard of care for MS patients.

WIENER MEDIZINISCHE WOCHENSCHRIFT (2022)

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Article Clinical Neurology

Machine learning classification of multiple sclerosis in children using optical coherence tomography

Beyza Ciftci Kavaklioglu et al.

Summary: This study demonstrates the feasibility of using machine learning based on OCT features to support the diagnosis of MS in children.

MULTIPLE SCLEROSIS JOURNAL (2022)

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Article Clinical Neurology

The Role of Optical Coherence Tomography Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis

Rachel C. Kenney et al.

Summary: This study validates the use of intereye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell + inner plexiform (GCIPL) thickness by spectral domain optical coherence tomography (SD-OCT) for identifying people with a history of unilateral optic neuritis (ON). Machine learning classification and a composite score combining OCT and visual measures improve the diagnostic capability of SD-OCT for distinguishing people with multiple sclerosis (PwMS) from controls. GCIPL IED is the best discriminator of unilateral ON history among PwMS.

NEUROLOGY (2022)

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Review Chemistry, Analytical

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Nida Aslam et al.

Summary: This article reviews the application of machine learning in diagnosing Multiple Sclerosis (MS). Researchers have developed various models using different types of data such as MRI and clinical data. SVM, RF, and CNN are identified as the most successful approaches. The article discusses the challenges in MS diagnosis, including differentiating the disease from others with similar symptoms, data confidentiality, interpretability, and the difficulty of collecting large reliable datasets. Moreover, opportunities such as secure platforms, better AI solutions, and combining different data types are discussed to improve the automated diagnosis of MS.

SENSORS (2022)

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Article Neuroimaging

Role of artificial intelligence in MS clinical practice

Raffaello Bonacchi et al.

Summary: Machine learning and deep learning, branches of artificial intelligence, have shown promising applications in the medical field, particularly in analyzing imaging data. Multiple sclerosis (MS) is an ideal candidate for applying AI techniques due to the significant role of MRI in its diagnosis and management. AI algorithms have advantages in automating tasks, analyzing more data efficiently, and achieving high accuracy. These algorithms have been used in MS diagnosis, prognosis, and treatment monitoring. Challenges include understanding the information selected by AI algorithms, validating results across multiple centers and over time, and addressing practical issues in hardware and software integration. Human supervision is crucial in optimizing the use of AI approaches.

NEUROIMAGE-CLINICAL (2022)

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Article Imaging Science & Photographic Technology

Graphical Image Region Extraction with K-Means Clustering and Watershed

Sandra Jardim et al.

Summary: Image segmentation is a crucial preprocessing step in automatic visual systems, impacting the effectiveness of follow-up tasks by extracting relevant image regions. Segmentation of color images, especially trademark graphic images with high noise and low resolution, remains a challenging problem. Hybrid approaches, such as combining K-Means and Watershed algorithms, show promising results in region extraction for diverse trademark images.

JOURNAL OF IMAGING (2022)

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Review Biotechnology & Applied Microbiology

Machine learning for precision medicine

Sarah J. MacEachern et al.

Summary: Precision medicine utilizes multi-modal data to make individualized treatment decisions, requiring advanced computer techniques such as machine learning to process and analyze large-scale complex datasets, ultimately enhancing understanding of human health and disease.

GENOME (2021)

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Article Computer Science, Artificial Intelligence

Structured layer surface segmentation for retina OCT using fully convolutional regression networks

Yufan He et al.

Summary: Optical coherence tomography (OCT) is a noninvasive imaging modality widely used for scanning the retina to analyze the retinal layers. A unified deep learning framework combining pixel classification and surface extraction steps can achieve smooth, continuous surfaces with topological constraints.

MEDICAL IMAGE ANALYSIS (2021)

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Review Clinical Neurology

Is MS affecting the CNS only? Lessons from clinic to myelin pathophysiology

Ellen Oudejans et al.

Summary: Multiple sclerosis is not only confined to the central nervous system, but may also involve the peripheral nervous system, presenting new challenges in understanding its origin and treatment.

NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION (2021)

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Article Biology

Early diagnosis of multiple sclerosis by OCT analysis using Cohen's d method and a neural network as classifier

E. Garcia-Martin et al.

Summary: This study aimed to diagnose early-stage multiple sclerosis by analyzing retinal layer thickness using swept-source optical coherence tomography. The research found that GCL++ had the greatest discriminatory capacity in early-stage MS, followed by retina, GCL+, and RNFL; choroidal thickness did not provide discriminatory capacity. Using OCT and artificial neural networks, it is possible to characterize structural alterations in the retina and diagnose early-stage MS with high accuracy.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Review Medicine, General & Internal

Diagnosis and Treatment of Multiple Sclerosis A Review

Marisa P. McGinley et al.

Summary: Multiple sclerosis is a neurodegenerative disease that affects the central nervous system, leading to physical disability, cognitive impairment and decreased quality of life. Current disease-modifying therapies can reduce relapse rates by 29% to 68% compared to placebo or active comparators.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2021)

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Article Clinical Neurology

Visual deficits and cognitive assessment of multiple sclerosis: confounder, correlate, or both?

Dejan Jakimovski et al.

Summary: The study found that both visual acuity and OCT-based measures (pRNFL and macular volume) were associated with cognitive performance in MS patients, with OCT-based measures also being significant predictors of cognitive status in MS patients. mGCIP was associated with cognitive performance in HCs.

JOURNAL OF NEUROLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

Oliver Diaz et al.

Summary: The vast amount of data generated by medical imaging systems today has led professionals to explore novel technologies such as artificial intelligence to efficiently handle and utilize the data. Proper preparation of medical images is crucial for the development of reliable AI algorithms, involving steps like image acquisition, de-identification, data curation, storage, and annotation. Open access tools and medical image repositories play important roles in these processes, with future work in this area focusing on further advancements.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)

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Article Clinical Neurology

Subclinical anterior optic pathway involvement in early multiple sclerosis and clinically isolated syndromes

Marco Pisa et al.

Summary: Optical coherence tomography (OCT) is increasingly important in evaluating patients with multiple sclerosis, showing neuro-retinal changes indicative of inflammatory and disease burden. Subclinical optic nerve involvement and thinning of ganglion cell layer in eyes without acute optic neuritis are associated with greater disease burden, reflecting early disease phases.

BRAIN (2021)

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Review Ophthalmology

Artificial Intelligence (AI) and Retinal Optical Coherence Tomography (OCT)

Mohammad Dahrouj et al.

Summary: Ophthalmology utilizes artificial intelligence for OCT image analysis to expand patient screening and facilitate clinical decision-making. Various AI algorithms, such as machine learning and deep learning, are applied in disease screening, diagnosis, management, and prognosis. However, challenges in quality and accuracy of datasets, generalizability, false negatives, and cultural acceptance may hinder AI adoption in ophthalmology.

SEMINARS IN OPHTHALMOLOGY (2021)

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Article Biology

Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography

Alberto Montolio et al.

Summary: Machine learning techniques using clinical and OCT data were utilized to develop two predictive models for MS diagnosis and disability course prediction. These models showed promising results in establishing early diagnosis and predicting the course of MS, highlighting the potential of RNFL thickness as a reliable MS biomarker.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Multidisciplinary Sciences

Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using optical coherence tomography angiography

Malgorzata Rogaczewska et al.

Summary: The study utilized spectral-domain optical coherence tomography (OCT) and OCT angiography (OCTA) to identify neurovascular alterations in NMOSD and MS eyes, finding differences in radial peripapillary capillary (RPC) vessel density and retinal nerve fiber layer thickness that may aid in differentiation. Using disease-specific intra-eye RPC vessel density reduction patterns, simple ratios were identified as effective discriminants between NMOSD and MS patients.

SCIENTIFIC REPORTS (2021)

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Review Computer Science, Theory & Methods

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi et al.

Summary: Deep learning has become the gold standard in the machine learning community, widely used in various domains and capable of learning massive data. Through a comprehensive survey, a better understanding of the most important aspects of deep learning is provided.

JOURNAL OF BIG DATA (2021)

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Review Health Care Sciences & Services

Deep learning-enabled medical computer vision

Andre Esteva et al.

Summary: The past decade has seen unprecedented progress in artificial intelligence, particularly in the field of medicine. Modern computer vision techniques powered by deep learning are revolutionizing medical applications, including imaging and video analysis. However, there are challenges to overcome for the real-world clinical deployment of these technologies.

NPJ DIGITAL MEDICINE (2021)

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Article Clinical Neurology

Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling

Fabio Pellegrini et al.

MULTIPLE SCLEROSIS JOURNAL (2020)

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Article Clinical Neurology

MS optic neuritis-induced long-term structural changes within the visual pathway

Marc Pawlitzki et al.

NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION (2020)

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Review Clinical Neurology

Differential diagnosis of multiple sclerosis and other inflammatory CNS diseases

Paula Wildner et al.

MULTIPLE SCLEROSIS AND RELATED DISORDERS (2020)

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Article Multidisciplinary Sciences

Automated Retinal Layer Segmentation Using Graph-based Algorithm Incorporating Deep-learning-derived Information

Zubin Mishra et al.

SCIENTIFIC REPORTS (2020)

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Article Multidisciplinary Sciences

Prediction of disease progression and outcomes in multiple sclerosis with machine learning

Mauro F. Pinto et al.

SCIENTIFIC REPORTS (2020)

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Article Clinical Neurology

Deep Learning-Based Method to Differentiate Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis

Hyunjin Kim et al.

FRONTIERS IN NEUROLOGY (2020)

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Article Cell Biology

Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning

Massimo Acquaviva et al.

CELL REPORTS MEDICINE (2020)

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Article Clinical Neurology

Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition

Clare Walton et al.

MULTIPLE SCLEROSIS JOURNAL (2020)

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Article Clinical Neurology

Peripapillary retinal nerve fibre layer as measured by optical coherence tomography is a prognostic biomarker not only for physical but also for cognitive disability progression in multiple sclerosis

Gabriel Bsteh et al.

MULTIPLE SCLEROSIS JOURNAL (2019)

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Article Clinical Neurology

Peripapillary retinal nerve fibre layer as measured by optical coherence tomography is a prognostic biomarker not only for physical but also for cognitive disability progression in multiple sclerosis

Gabriel Bsteh et al.

MULTIPLE SCLEROSIS JOURNAL (2019)

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Review Clinical Neurology

Medical ethics considerations on artificial intelligence

Kadircan H. Keskinbora

JOURNAL OF CLINICAL NEUROSCIENCE (2019)

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Article Clinical Neurology

Changes in the Risk of Reaching Multiple Sclerosis Disability Milestones In Recent Decades: A Nationwide Population-Based Cohort Study in Sweden

Omid Beiki et al.

JAMA NEUROLOGY (2019)

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Article Multidisciplinary Sciences

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

Amaya Perez del Palomar et al.

PLOS ONE (2019)

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Article Chemistry, Analytical

Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features

Carlo Cavaliere et al.

SENSORS (2019)

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Article Clinical Neurology

Retinal measurements predict 10-year disability in multiple sclerosis

Alissa Rothman et al.

ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY (2019)

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Article Engineering, Biomedical

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

Ryan Poplin et al.

NATURE BIOMEDICAL ENGINEERING (2018)

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Article Medicine, General & Internal

Multiple sclerosis

Massimo Filippi et al.

NATURE REVIEWS DISEASE PRIMERS (2018)

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Review Medicine, General & Internal

Medical Imaging: From Roentgen to the Digital Revolution, and Beyond

Eyal Bercovich et al.

RAMBAM MAIMONIDES MEDICAL JOURNAL (2018)

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Review Clinical Neurology

Retinal ganglion cell analysis in multiple sclerosis and optic neuritis: a systematic review and meta-analysis

Josefine Britze et al.

JOURNAL OF NEUROLOGY (2017)

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Article Computer Science, Artificial Intelligence

A survey on deep learning in medical image analysis

Geert Litjens et al.

MEDICAL IMAGE ANALYSIS (2017)

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Article Multidisciplinary Sciences

Kynurenine pathway metabolomics predicts and provides mechanistic insight into multiple sclerosis progression

Chai K. Lim et al.

SCIENTIFIC REPORTS (2017)

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Article Remote Sensing

A supervised hierarchical segmentation of remote-sensing images using a committee of multi-scale convolutional neural networks

Essa Basaeed et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2016)

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Article Clinical Neurology

Dynamics of Retinal Injury after Acute Optic Neuritis

Inigo Gabilondo et al.

ANNALS OF NEUROLOGY (2015)

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Article Biology

Discrimination of retinal images containing bright lesions using sparse coded features and SVM

Desire Sidibe et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2015)

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Review Clinical Neurology

International consensus diagnostic criteria for neuromyelitis optica spectrum disorders

Dean M. Wingerchuk et al.

NEUROLOGY (2015)

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Article Multidisciplinary Sciences

DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm

Othman Soufan et al.

PLOS ONE (2015)

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Article Ophthalmology

Artificial Neural Network Techniques to Improve the Ability of Optical Coherence Tomography to Detect Optic Neuritis

Elena Garcia-Martin et al.

SEMINARS IN OPHTHALMOLOGY (2015)

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Article Ophthalmology

Neural networks to identify multiple sclerosis with optical coherence tomography

Elena Garcia-Martin et al.

ACTA OPHTHALMOLOGICA (2013)

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Article Clinical Neurology

Neuro-ophthalmologic evaluation, quality of life, and functional disability in patients with MS

Elena Garcia-Martin et al.

NEUROLOGY (2013)

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Article Immunology

Decreased serum levels of nucleolin protein fragment, as analyzed by bead-based proteomic technology, in multiple sclerosis patients compared to controls

Jianghong Liu et al.

JOURNAL OF NEUROIMMUNOLOGY (2012)

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Letter Clinical Neurology

Reply: Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography

Shiv Saidha et al.

BRAIN (2011)

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Article Multidisciplinary Sciences

High Resolution Spectral Domain Optical Coherence Tomography (SD-OCT) in Multiple Sclerosis: The First Follow Up Study over Two Years

Nermin Serbecic et al.

PLOS ONE (2011)

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Editorial Material Ophthalmology

Spectral-Domain Optical Coherence Tomography: A Comparison of Modern High-Resolution Retinal Imaging Systems

Daniel F. Kiernan et al.

AMERICAN JOURNAL OF OPHTHALMOLOGY (2010)

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Review Clinical Neurology

The changing demographic pattern of multiple sclerosis epidemiology

Nils Koch-Henriksen et al.

LANCET NEUROLOGY (2010)

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Correction Clinical Neurology

Optical coherence tomography in multiple sclerosis: a systematic review and meta-analysis (vol 9, pg 921, 2010)

A. Petzold et al.

LANCET NEUROLOGY (2010)

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Review Clinical Neurology

Optical coherence tomography in multiple sclerosis: a systematic review and meta-analysis

Axel Petzold et al.

LANCET NEUROLOGY (2010)

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Review Medicine, General & Internal

Multiple sclerosis

Alastair Compston et al.

LANCET (2008)

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Article Clinical Neurology

Optical coherence tomography and disease subtype in multiple sclerosis

M. Pulicken et al.

NEUROLOGY (2007)

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Article Clinical Neurology

Retinal ganglion cell loss induced by acute optic neuritis in a relapsing model of multiple sclerosis

Kenneth S. Shindler et al.

MULTIPLE SCLEROSIS JOURNAL (2006)

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Article Management

Solving feature subset selection problem by a Parallel Scatter Search

FG López et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2006)

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