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

Miguel-Jiménez, J. M.; Roldán, María José Anitua; Pérez-Rico, C.; Ortiz, Miguel; Boquete, Luciano; Blanco, R.

doi:10.1038/s41598-021-81826-z

Cited by 5 publications

(3 citation statements)

References 42 publications

(19 reference statements)

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“…The machine-learning techniques applied to MS have mainly targeted early diagnosis, including the analysis of potential conversion of possible preliminary stages of the disease, such as radiologically isolated syndrome, into definitive MS [ 46 ], or the prediction of disease progression and outcomes [ 47 ]. The type of data analyzed and the analysis and classification tools employed vary between studies.…”

Section: Introductionmentioning

confidence: 99%

“…The type of data analyzed and the analysis and classification tools employed vary between studies. By way of example, in [ 46 ], multifocal visual-evoked potential features are classified with an RUSBoost boosting-based sampling algorithm; [ 47 ] analyzes clinical information (age, onset age, initial MS manifestations, and clinical and examination findings that led to the diagnosis, such as MRI, evoked potentials, etc.) using four classifiers (support vector machine (SVM), k-nearest neighbors (k-NN), a decision tree, and linear regression); or [ 48 ], which uses recurrent neural networks to predict disability progression in MS patients over a two-year horizon.…”

Section: Introductionmentioning

confidence: 99%

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

López-Dorado

Ortiz

Satué

et al. 2021

Sensors

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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. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN’s training set. Results: The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0. Conclusions: Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

López-Dorado

Ortiz

Satué

et al. 2021

Sensors

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“…Other potential molecular biomarkers include serum/CSF glial fibrillary acidic protein, and serum-based microribonucleic acids (miRNAs) (49)(50)(51). Finally, abnormal visual evoked responses (52,53) and optical coherence tomography (54) may be biomarkers associated with abnormalities in the visual pathways.…”

Section: Serum Csf and Other Opportunities For Biomarker Discoverymentioning

confidence: 99%

The Multiple Sclerosis Prodrome: Evidence to Action

2022

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A growing body of work points toward the existence of a clinically symptomatic prodromal phase in multiple sclerosis (MS) that might span 5–10 years or more. A prodrome is an early set of signs or symptoms predating the onset of classical disease, which in turn predates a definitive diagnosis. Evidence for a prodromal phase in MS could have major implications for prevention, earlier recognition and treatment, as well as an improved disease course or prognosis. This Perspective provides a succinct overview of the recent advances in our understanding of the MS prodrome and current key challenges. Many of the MS prodromal features characterized thus far are non-specific and are common in the general population; no single feature alone is sufficient to identify an individual with prodromal MS. Biomarkers may increase specificity and accuracy for detecting individuals in the MS prodromal phase, but are yet to be discovered or formally validated. Progress made in the elucidation of prodromal phases in other neurological and immune-mediated diseases suggests that these barriers can be overcome. Therefore, while knowledge of a prodromal phase in MS remains nascent, how best to move from the rapidly growing evidence to research-related action is critical. Immediate implications include refining the concept of the MS continuum to include a prodromal phase. This will help inform the true “at risk” period when considering exposures that might cause MS. Major long-term implications include the earlier recognition of MS, improved prognosis, through earlier disease management, and the future possibility of MS disease prevention.

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Conversion Predictors of Clinically Isolated Syndrome to Multiple Sclerosis in Mexican Patients: A Prospective Study

Chavarria,

Espinosa-Ramírez,

Sotelo

et al. 2023

Archives of Medical Research

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

Cited by 5 publications

References 42 publications

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

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

The Multiple Sclerosis Prodrome: Evidence to Action

Conversion Predictors of Clinically Isolated Syndrome to Multiple Sclerosis in Mexican Patients: A Prospective Study

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