Publications by authors named "L Salgado Boquete"
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.
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.
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- The study focuses on identifying new biomarkers for the early diagnosis of multiple sclerosis (MS) using advanced techniques like spectral-domain optical coherence tomography (OCT) and artificial intelligence.
- Researchers analyzed retinal thickness and differences between the eyes in patients with relapsing-remitting MS and healthy controls to find specific patterns that could indicate MS.
- The results showed promising accuracy for diagnosing MS: using a convolutional neural network, the study achieved an accuracy of 87%, suggesting that neuroretinal structure analysis might be an important addition to current diagnostic methods for MS.
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.
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.