Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system, especially the brain, spinal cord, and optic nerve. Diagnosis of this disease is a very complex process and generally requires a lot of time. In addition, treatments are applied without any information on the disability course in each MS patient. For these two reasons, the objective of this study was to improve the MS diagnosis and predict the long-term course of disability in MS patients based on clinical data and retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). A total of 104 healthy controls and 108 MS patients, 82 of whom had a 10-year follow-up, were enrolled. Classification algorithms 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 to develop two predictive models: MS diagnosis model and MS disability course prediction model. For MS diagnosis, the best result was obtained using EC (accuracy: 87.7%; sensitivity: 87.0%; specificity: 88.5%; precision: 88.7%; AUC: 0.8775). In line with this good performance, the accuracy was 85.4% using k-NN and 84.4% using SVM. And, for long-term prediction of MS disability course, LSTM recurrent neural network was the most appropriate classifier (accuracy: 81.7%; sensitivity: 81.1%; specificity: 82.2%; precision: 78.9%; AUC: 0.8165). The use of MLR, SVM and k-NN also showed a good performance (AUC ≥ 0.8). This study demonstrated that machine learning techniques, using clinical and OCT data, can help establish an early diagnosis and predict the course of MS. This advance could help clinicians select more specific treatments for each MS patient. Therefore, our findings underscore the potential of RNFL thickness as a reliable MS biomarker.

To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer–Inner plexiform layer (GCL+) damage in patients with multiple sclerosis and to use the SS-OCT as a biomarker to early predict this disease. Patients with relapsing-remitting MS (n = 80) and age-matched healthy controls (n = 180) were enrolled. Different protocols from the DRI SS-OCT Triton system were used to obtain the RNFL and GCL+ thicknesses in both eyes. Macular and peripapilar areas were analyzed to detect the zones with higher thickness decrease. The performance of different machine learning techniques (decision trees, multilayer perceptron and support vector machine) for identifying RNFL and GCL+ thickness loss in patients with MS were evaluated. Receiver-operating characteristic (ROC) curves were used to display the ability of the different tests to discriminate between MS and healthy eyes in our population. Machine learning techniques provided an excellent tool to predict MS disease using SS-OCT data. In particular, the decision trees obtained the best prediction (97.24%) using RNFL data in macular area and the area under the ROC curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998. Moreover, it was obtained that the most significant area of the RNFL to predict MS is the macula just surrounding the fovea. On the other hand, in our study, GCL+ did not contribute to predict MS and the different machine learning techniques performed worse in this layer than in RNFL. Measurements of RNFL thickness obtained with SS-OCT have an excellent ability to differentiate between healthy controls and patients with MS. Thus, the use of machine learning techniques based on these measures can be a reliable tool to help in MS diagnosis.

To evaluate the ability of new Swept source (SS) optical coherence tomography (OCT) technology to detect changes in retinal and choroidal thickness in patients with Parkinson's disease (PD). Observational case-control cross sectional study, developed from January to May 2016. In total, 50 eyes from 50 patients diagnosed with PD and 54 eyes of 54 healthy controls underwent retinal and choroidal assessment using SS DRI Triton OCT (Topcon), using the 3D Wide protocol. Total macular thickness and peripapillary data (retinal, ganglion cell layer [GCL+, GCL++] and retinal nerve fiber layer [RNFL] thickness) were analyzed. Macular and peripapillary choroidal thickness was evaluated (Figure 1). Significant peripapillary retinal thinning was observed in PD patients in total average (p = 0.017), in the nasal (p = 0.038) and temporal (p = 0.004) quadrants and in superotemporal (p = 0.004), nasal (p = 0.039), inferotemporal (p = 0.019), and temporal (p = 0.003) sectors. RNFL and GCL ++ thickness showed a significant reduction in the inferotemporal sector (p = 0.026 and 0.009, respectively). No differences were observed in macular retinal thickness between controls and patients. Choroidal thickness was found to have increased in all sectors in PD patients compared with controls, both in the macular (inner nasal, p = 0.015; inner inferior, p = 0.030; outer nasal, p = 0.012; outer inferior, p = 0.049) and the peripapillary area (total thickness, p = 0.011; nasal, p = 0.025; inferior, p = 0.007; temporal, p = 0.003; inferotemporal, p = 0.003; inferonasal, p = 0.016) Conclusion: New SS technology for OCT devices detects retinal thinning in PD patients, providing increased depth analysis of the choroid in these patients. The choroid in PD may present increased thickness compared to healthy individuals; however, more studies and histological analysis are needed to corroborate our findings.

Purpose: To evaluate visual and retinal changes in patients with bipolar disorder. To analyze the correlation between structural changes and visual function parameters.Methods: Thirty patients with bipolar disorder and 80 healthy controls underwent visual function evaluation with Early Treatment Diabetic Retinopathy Study charts at 100%, 2.50%, and 1.25% contrast, Pelli-Robson chart, and color vision Farnsworth and Lanthony tests. Analysis of the different retinal layers was performed using Spectralis optical coherence tomography with automated segmentation software. Correlation analysis between structural and functional parameters was conducted.Results: Patients with bipolar disorder presented worse color vision compared with controls (Lanthony's index,P= 0.002). Full macular thickness, the retinal nerve fiber layer (RNFL), ganglion cell layer, and inner plexiform layer were reduced in patients compared with healthy individuals (P< 0.005). The inner nuclear layer was significantly thickened in patients (P< 0.005). Peripapillary RNFL thickness was reduced in all temporal sectors (P< 0.005). Significant correlations were found between visual acuity and the RNFL thickness, the Pelli-Robson score and the inner plexiform layer, and between the Lanthony's color index and the ganglion cell layer thickness.Conclusion: Patients with bipolar disorder present quantifiable thinning of the macular RNFL, ganglion cell layer, and inner plexiform layer, as well as in the peripapillary RNFL thickness, and increasing thinning in the inner nuclear layer.

Background: Diabetic retinopathy (DR) is the leading cause of legal blindness in the working population in developed countries. Optical coherence tomography (OCT) angiography (OCTA) has risen as an essential tool in the diagnosis and control of diabetic patients, with and without DR, allowing visualisation of the retinal and choroidal microvasculature, their qualitative and quantitative changes, the progression of vascular disease, quantification of ischaemic areas, and the detection of preclinical changes. The aim of this article is to analyse the current applications of OCTA and provide an updated overview of them in the evaluation of DR. Methods: A systematic literature search was performed in PubMed and Embase, including the keywords “OCTA” OR “OCT angiography” OR “optical coherence tomography angiography” AND “diabetes” OR “diabetes mellitus” OR “diabetic retinopathy” OR “diabetic maculopathy” OR “diabetic macular oedema” OR “diabetic macular ischaemia”. Of the 1456 studies initially identified, 107 studies were screened after duplication, and those articles that did not meet the selection criteria were removed. Finally, after looking for missing data, we included 135 studies in this review. Results: We present the common and distinctive findings in the analysed papers after the literature search including the diagnostic use of OCTA in diabetes mellitus (DM) patients. We describe previous findings in retinal vascularization, including microaneurysms, foveal avascular zone (FAZ) changes in both size and morphology, changes in vascular perfusion, the appearance of retinal microvascular abnormalities or new vessels, and diabetic macular oedema (DME) and the use of deep learning technology applied to this disease. Conclusion: OCTA findings enable the diagnosis and follow-up of DM patients, including those with no detectable lesions with other devices. The evaluation of retinal and choroidal plexuses using OCTA is a fundamental tool for the diagnosis and prognosis of DR.