Physiological changes in retinal layers thicknesses measured with swept source optical coherence tomography

Viladés, Elisa; Palomar, Amaya Pérez-del; Cegoñino, José; Obís, J.; Satué, María; Orduna, Elvira; Júlvez, Luis Pablo; Ciprés, M.; García‐Martín, Elena

doi:10.1371/journal.pone.0240441

Cited by 5 publications

(3 citation statements)

References 30 publications

(40 reference statements)

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“…As expected, the general trend was that both volume and thickness were higher in healthy controls than in MS patients. It is well known that RNFL thinning occurs as part of normal aging, 59 but an additional thinning occurs as a pathological consequence of MS. In the early stages of the disease, demyelination and axonal transection occur.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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Machine learning approaches in diagnosis and prognosis of multiple sclerosis (MS) were analysed using retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). A cross-sectional study (72 MS patients and 30 healthy controls) was used for diagnosis. These 72 MS patients were involved in a 10-year longitudinal follow-up study for prognostic purposes. Structural measurements of RNFL thickness were performed using different Spectralis OCT protocols: fast macular thickness protocol to measure macular RNFL, and fast RNFL thickness protocol and fast RNFL-N thickness protocol to measure peripapillary RNFL. Binary classifiers such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested. For MS diagnosis, the best acquisition protocol was fast macular thickness protocol using k-NN (accuracy: 95.8%; sensitivity: 94.4%; specificity: 97.2%; precision: 97.1%; AUC: 0.958). For MS prognosis, our model with a 3-year follow up to predict disability progression 8 years later was the best predictive model. DT performed best for fast macular thickness protocol (accuracy: 91.3%; sensitivity: 90.0%; specificity: 92.5%; precision: 92.3%; AUC: 0.913) and SVM for fast RNFL-N thickness protocol (accuracy: 91.3%; sensitivity: 87.5%; specificity: 95.0%; precision: 94.6%; AUC: 0.913). This work concludes that measurements of RNFL thickness obtained with Spectralis OCT have a good ability to diagnose MS and to predict disability progression in MS patients. This machine learning approach would help clinicians to have valuable information.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…It receives visual information and subsequently transmits it through the optic nerve to the brain . During the progression of retinal diseases, common indicators emerge, including alterations in retinal layer thickness, pathological growth of blood vessel, and an inflammatory response. − The discovery and identification of additional targets for retinal diseases have expedited mechanism-based drug discovery. , This section delineates the pathological characteristics of three significant retinal diseases and the therapeutic drugs designed to target them.…”

Section: Retinal Disease and Therapeutic Drugsmentioning

confidence: 99%

Smart Nanocarriers for the Treatment of Retinal Diseases

Liu,

Yan,

Huang

et al. 2024

ACS Appl. Bio Mater.

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Retinal diseases, such as age-related macular degeneration, diabetic retinopathy, and retinoblastoma, stand as the leading causes of irreversible vision impairment and blindness worldwide. Effectively administering drugs for retinal diseases poses a formidable challenge due to the presence of complex ocular barriers and elimination mechanisms. Over time, various approaches have been developed to fabricate drug delivery systems for improving retinal therapy including virus vectors, lipid nanoparticles, and polymers. However, conventional nanocarriers encounter issues related to the controllability, efficiency, and safety in the retina. Therefore, the development of smart nanocarriers for effective or more invasive long-term treatment remains a desirable goal. Recently, approaches have surfaced for the intelligent design of nanocarriers, leveraging specific responses to external or internal triggers and enabling multiple functions for retinal therapy such as topical administration, prolonged drug release, and site-specific drug delivery. This Review provides an overview of prevalent retinal pathologies and related pharmacotherapies to enhance the understanding of retinal diseases. It also surveys recent developments and strategies employed in the intelligent design of nanocarriers for retinal disease. Finally, the challenges of smart nanocarriers in potential clinical retinal therapeutic applications are discussed to inspire the next generation of smart nanocarriers.

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“… Representation of retinal layers evaluated by DRI OCT Triton. Adaptation from Vilades E. et al 2020 [ 35 ]. …”

Section: Figurementioning

confidence: 99%

Retinal and Choroidal Thinning—A Predictor of Coronary Artery Occlusion?

et al. 2022

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Introduction. Optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) allowed visualization of retina and choroid to nearly the capillary level; however, the relationship between systemic macrovascular status and retinal microvascular changes is not yet known well. Aim. Our purpose was to assess the impact of retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) parameters on prediction of coronary heart disease (CHD) in acute myocardial infarction (MI) and chronic three vessel disease (3VD) groups. Methods. This observational study included 184 patients—26 in 3VD, 76 in MI and 82 in healthy participants groups. Radial scans of the macula and OCTA scans of the central macula (superficial (SCP) and deep (DCP) capillary plexuses) were performed on all participants. All participants underwent coronary angiography. Results. Patients in MI groups showed decreased parafoveal total retinal thickness as well as GCL+ retinal thickness. Outer circle total retinal thickness and GCL+ retinal thickness were lowest in the 3VD group. The MI group had thinner, while 3VD the thinnest, choroid. A decrease in choroidal thickness and vascular density could predict 3VD. Conclusions. A decrease in retinal and choroidal thickness as well as decreased vascular density in the central retinal region may predict coronary artery disease. OCT and OCTA could be a significant, safe, and noninvasive tool for the prediction of coronary artery disease.

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Physiological changes in retinal layers thicknesses measured with swept source optical coherence tomography

Cited by 5 publications

References 30 publications

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

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

Smart Nanocarriers for the Treatment of Retinal Diseases

Retinal and Choroidal Thinning—A Predictor of Coronary Artery Occlusion?

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