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

Background/objectives: Study of retinal structure based on optical coherence tomography (OCT) data can facilitate early diagnosis of relapsing-remitting multiple sclerosis (RRMS). Although artificial intelligence can provide highly reliable diagnoses, the results obtained must be explainable.

Subjects/methods: The study included 79 recently diagnosed RRMS patients and 69 age matched healthy control subjects.

Differential Study of Retinal Thicknesses in the Eyes of Alzheimer's Patients, Multiple Sclerosis Patients and Healthy Subjects.

Multiple sclerosis (MS) and Alzheimer's disease (AD) cause retinal thinning that is detectable in vivo using optical coherence tomography (OCT). To date, no papers have compared the two diseases in terms of the structural differences they produce in the retina. The purpose of this study is to analyse and compare the neuroretinal structure in MS patients, AD patients and healthy subjects using OCT.

Objective Diagnosis of Fibromyalgia Using Neuroretinal Evaluation and Artificial Intelligence.

Background/objective: This study aims to identify objective biomarkers of fibromyalgia (FM) by applying artificial intelligence algorithms to structural data on the neuroretina obtained using swept-source optical coherence tomography (SS-OCT).

Method: The study cohort comprised 29 FM patients and 32 control subjects. The thicknesses of complete retina, 3 retinal layers [ganglion cell layer (GCL+), GCL++ (between the inner limiting membrane and the inner nuclear layer boundaries) and retinal nerve fiber layer (RNFL)] and choroid in 9 areas around the macula were obtained using SS-OCT.

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

Background: The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT).

Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used.

Diagnosis of multiple sclerosis using multifocal ERG data feature fusion.

The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI-port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants).

Using advanced analysis of multifocal visual-evoked potentials to evaluate the risk of clinical progression in patients with radiologically isolated syndrome.

This study aimed to assess the role of multifocal visual-evoked potentials (mfVEPs) as a guiding factor for clinical conversion of radiologically isolated syndrome (RIS). We longitudinally followed a cohort of 15 patients diagnosed with RIS. All subjects underwent thorough ophthalmological, neurological and imaging examinations.

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

Background: The consequences of inflammation, demyelination, axonal degeneration and neuronal loss in the central nervous system, typical of the development of multiple sclerosis (MS), are manifested in thinning of the retina and optic nerve. The purpose of this work is to diagnose early-stage MS patients based on analysis of retinal layer thickness obtained by swept-source optical coherence tomography (SS-OCT).

Method: OCT (Triton® SS-OCT device -Topcon, Tokyo, Japan-) recordings were obtained from 48 control subjects and 48 recently diagnosed MS patients.

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis.

The purpose of this paper is to record and analyze induced gamma-band activity (GBA) (30-60 Hz) in cerebral motor areas during imaginary movement and to compare it quantitatively with activity recorded in the same areas during actual movement using a simplified electroencephalogram (EEG). Brain activity (basal activity, imaginary motor task and actual motor task) is obtained from 12 healthy volunteer subjects using an EEG (Cz channel). GBA is analyzed using the mean power spectral density (PSD) value.

Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis.

As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined.

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

The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of optic neuritis and forty-eight healthy control subjects were selected. Swept-source optical coherence tomography (SS-OCT) was performed using a DRI (deep-range imaging) Triton OCT device (Topcon Corp.

Diagnostic ability of multifocal electroretinogram in early multiple sclerosis using a new signal analysis method.

Purpose: To determine if a novel analysis method will increase the diagnostic value of the multifocal electroretinogram (mfERG) in diagnosing early-stage multiple sclerosis (MS).

Methods: We studied the mfERG signals of OD (Oculus Dexter) eyes of fifteen patients diagnosed with early-stage MS (in all cases < 12 months) and without a history of optic neuritis (ON) (F:M = 11:4), and those of six controls (F:M = 3:3). We obtained values of amplitude and latency of N1 and P1 waves, and a method to assess normalized root-mean-square error (FNRMSE) between model signals and mfERG recordings was used.

Identification of clusters in multifocal electrophysiology recordings to maximize discriminant capacity (patients vs. control subjects).

Purpose: To propose a new method of identifying clusters in multifocal electrophysiology (multifocal electroretinogram: mfERG; multifocal visual-evoked potential: mfVEP) that conserve the maximum capacity to discriminate between patients and control subjects.

Methods: The theoretical framework proposed creates arbitrary N-size clusters of sectors. The capacity to discriminate between patients and control subjects is assessed by analysing the area under the receiver operator characteristic curve (AUC).

A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings.

Introduction: The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects.

Patients: MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes).

Coding Prony's method in MATLAB and applying it to biomedical signal filtering.

Background: The response of many biomedical systems can be modelled using a linear combination of damped exponential functions. The approximation parameters, based on equally spaced samples, can be obtained using Prony's method and its variants (e.g.

Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis.

Objective: To study the performance of multifocal-visual-evoked-potential (mfVEP) signals filtered using empirical mode decomposition (EMD) in discriminating, based on amplitude, between control and multiple sclerosis (MS) patient groups, and to reduce variability in interocular latency in control subjects.

Methods: MfVEP signals were obtained from controls, clinically definitive MS and MS-risk progression patients (radiologically isolated syndrome (RIS) and clinically isolated syndrome (CIS)). The conventional method of processing mfVEPs consists of using a 1-35 Hz bandpass frequency filter (XDFT).

Induced gamma band activity from EEG as a possible index of training-related brain plasticity in motor tasks.

The aim of this study was proposing gamma band activity (GBA) as an index of training-related brain plasticity in the motor cortex. Sixteen controls underwent an experimental session where electroencephalography (EEG) activity was recorded at baseline (resting) and during a motor task (hand movements). GBA was obtained from the EEG data at baseline and during the task.

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition.

The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel.

Multifocal visual evoked potentials for quantifying optic nerve dysfunction in patients with optic disc drusen.

Purpose: To explore the applicability of multifocal visual evoked potentials (mfVEPs) for research and clinical diagnosis in patients with optic disc drusen (ODD). This is the first assessment of mfVEP amplitude in patients with ODD.

Methods: MfVEP amplitude and latency from 33 patients with ODD and 22 control subjects were examined.

A signal-to-noise-ratio-based analysis of multifocal visual-evoked potentials in multiple sclerosis risk assessment.

Objective: To study the value of using the signal-to-noise ratio (SNR) of multifocal visual-evoked potentials (mfVEPs) in assessment of subjects at risk of developing multiple sclerosis (MS).

Methods: MfVEP signals were obtained from 15 patients with radiologically isolated syndrome (RIS), from 28 patients with clinically isolated syndrome (CIS), from 28 with clinically definite MS and from 24 control subjects. The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON).

Induced Gamma-Band Activity During Voluntary Movement: EEG Analysis for Clinical Purposes.

Objective: Propose a simplified method applicable in routine clinical practice that uses EEG to assess induced gamma-band activity (GBA) in the 30-90 Hz frequency range in cerebral motor areas.

Design: EEG recordings (25 healthy subjects) of cerebral activity (at rest, motor task). GBA was obtained as power spectral density (PSD).

Continuous-wavelet-transform analysis of the multifocal ERG waveform in glaucoma diagnosis.

The vast majority of multifocal electroretinogram (mfERG) signal analyses to detect glaucoma study the signals' amplitudes and latencies. The purpose of this paper is to investigate application of wavelet analysis of mfERG signals in diagnosis of glaucoma. This analysis method applies the continuous wavelet transform (CWT) to the signals, using the real Morlet wavelet.

Software for analysing multifocal visual evoked potential signal latency progression.

Background: This paper describes a new non-commercial software application (mfVEP(2)) developed to process multifocal visual-evoked-potential (mfVEP) signals in latency (monocular and interocular) progression studies.

Method: The software performs analysis by cross-correlating signals from the same patients. The criteria applied by the software include best channels, signal window, cross-correlation limits and signal-to-noise ratio (SNR).

Filtering mfVEP signals using Wiener filter.

The multifocal visual-evoked-potential (mfVEP) signals are filtered using the Wiener filter combined with a Fast Fourier Transform and their signal-to-noise ratios are compared against those of unfiltered signals (RAW data) and those of signals filtered using the traditional method (FFT data). The Wiener filter improves the original signals' SNR by 37.49%, while the FFT improves the SNR by 20.