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., Tokyo, Japan). Mean values (right and left eye) for macular thickness (retinal and choroidal layers) and peripapillary area (retinal nerve fibre layer, retinal, ganglion cell layer—GCL, and choroidal layers) were compared between both groups. Based on the analysis of the area under the receiver operator characteristic curve (AUC), the 3 variables with the greatest discriminant capacity were selected to form the feature vector. A SVM was used as an automatic classifier, obtaining the confusion matrix using leave-one-out cross-validation. Classification performance was assessed with Matthew’s correlation coefficient (MCC) and the AUC_CLASSIFIER. The most discriminant variables were found to be the total GCL++ thickness (between inner limiting membrane to inner nuclear layer boundaries), evaluated in the peripapillary area and macular retina thickness in the nasal quadrant of the outer and inner rings. Using the SVM classifier, we obtained the following values: MCC = 0.81, sensitivity = 0.89, specificity = 0.92, accuracy = 0.91, and AUC_CLASSIFIER = 0.97. Our findings suggest that it is possible to classify control subjects and MS patients without previous optic neuritis by applying machine-learning techniques to study the structural neurodegeneration in the retina.

To evaluate the inner retinal layers in fibromyalgia (FM) patients compared to control subjects using posterior pole protocol (PPole) analysis in optical coherence tomography (OCT) and to correlate...

The prevalence of neurodegenerative diseases (NDDs) such as Alzheimer's (AD), Parkinson's (PD), Essential tremor (ET), and Multiple Sclerosis (MS) is increasing alongside the aging population. Recent studies suggest that these disorders can be identified through retinal imaging, allowing for early detection and monitoring via Optical Coherence Tomography (OCT) scans. This study is at the forefront of research, pioneering the application of multi-view OCT and 3D information to the neurological diseases domain. Our methodology consists of two main steps. In the first one, we focus on the segmentation of the retinal nerve fiber layer (RNFL) and a class layer grouping between the ganglion cell layer and Bruch's membrane (GCL-BM) in both macular and optic disc OCT scans. These are the areas where changes in thickness serve as a potential indicator of NDDs. The second phase is to select patients based on information about the retinal layers. We explore how the integration of both views (macula and optic disc) improves each screening scenario: Healthy Controls (HC) vs. NDD, AD vs. NDD, ET vs. NDD, MS vs. NDD, PD vs. NDD, and a final multi-class approach considering all four NDDs. For the segmentation task, we obtained satisfactory results for both 2D and 3D approaches in macular segmentation, in which 3D performed better due to the inclusion of depth and cross-sectional information. As for the optic disc view, transfer learning did not improve the metrics over training from scratch, but it did provide a faster training. As for screening, 3D computational biomarkers provided better results than 2D ones, and multi-view methods were usually better than the single-view ones. Regarding separability among diseases, MS and PD were the ones that provided better results in their screening approaches, being also the most represented classes. In conclusion, our methodology has been successfully validated with an extensive experimentation of configurations, techniques and OCT views, becoming the first multi-view analysis that merges data from both macula-centered and optic disc-centered perspectives. Besides, it is also the first effort to examine key retinal layers across four major NDDs within the framework of pathological screening.

Background. To evaluate the neuroretina and retinal vasculature of fibromyalgia (FM) patients and calculate a linear discriminant function (LDF) to improve retinal parameters’ contribution to FM diagnosis. Methods. Fifty FM patients and 232 healthy controls underwent retinal evaluation using swept-source optical coherence tomography (SS-OCT) angiography (Triton plus; Topcon) and spectral domain OCT (SD-OCT) (Spectralis; Heidelberg). The macular (m) and peripapillary (p) retinal nerve fibre layer (RNFL) and ganglion cell layer (GCL) were assessed, as was the macular vascular density. A logistic regression analysis was performed, and an LDF was calculated to evaluate OCT’s contribution to FM diagnosis. Results. With Triton OCT, the patients presented pRNFL thinning in the temporal sector ( ). Spectralis OCT measurements showed decreased pRNFL in patients in the following sectors: superonasal, ; nasal, ; inferonasal, ; temporal, ; and inferotemporal, . No significant differences were observed in the macular vascular plexus between patients and controls. However, vascular density in the superior sector showed a strong inverse correlation with disease duration (r = −0.978, ). The LDF calculated for Spectralis OCT yielded an area under the ROC curve of 0.968. Conclusions. FM patients present RNFL thinning observable using SS- and SD-OCT. However, these patients show similar vascular density in the macular area to healthy controls. The LDF that combines several RNFL parameters obtained using Spectralis OCT gives this device a powerful ability to differentiate between healthy individuals and individuals with FM.

Purpose of the study: To assess the ability of the new Spectralis Optical Coherence Tomography (OCT) Posterior Pole protocol to detect degeneration of the inner retinal layers in patients with bipolar disorder (BD) and to assess the correlation between the neuroretinal thickness and disease duration.