Differential Study of Retinal Thicknesses in the Eyes of Alzheimer's Patients, Multiple Sclerosis Patients and Healthy 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. Spectral domain OCT was performed on 21 AD patients, 33 MS patients and 19 control subjects using the Posterior Pole protocol. The area under the receiver operating characteristic (AUROC) curve was used to analyse the differences between the cohorts in nine regions of the retinal nerve fibre layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL) and outer nuclear layer (ONL). The main differences between MS and AD are found in the ONL, in practically all the regions analysed (AUROCFOVEAL = 0.80, AUROCPARAFOVEAL = 0.85, AUROCPERIFOVEAL = 0.80, AUROC_PMB = 0.77, AUROCPARAMACULAR = 0.85, AUROCINFERO_NASAL = 0.75, AUROCINFERO_TEMPORAL = 0.83), and in the paramacular zone (AUROCPARAMACULAR = 0.75) and infero-temporal quadrant (AUROCINFERO_TEMPORAL = 0.80) of the GCL. In conclusion, our findings suggest that OCT data analysis could facilitate the differential diagnosis of MS and AD.

Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomography.

BACKGROUND: Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily understandable explanations. MATERIALS AND METHODS: A total of 216 eyes from 111 healthy controls and 100 eyes from 59 patients with relapsing-remitting MS were enrolled. The feature set was obtained from the thickness of the ganglion cell layer (GCL) and the retinal nerve fiber layer (RNFL). Measurements were acquired by the novel Posterior Pole protocol from Spectralis Optical Coherence Tomography (OCT) device. We compared two black-box methods (gradient boosting and random forests) with a glass-box method (explainable boosting machine). Explainability was studied using SHAP for the black-box methods and the scores of the glass-box method. RESULTS: The best-performing models were obtained for the GCL layer. Explainability pointed out to the temporal location of the GCL layer that is usually broken or thinning in MS and the relationship between low thickness values and high probability of MS, which is coherent with clinical knowledge. CONCLUSIONS: The insights on how to use explainability shown in this work represent a first important step toward a trustworthy computer-aided solution for the diagnosis of MS with OCT.

Evaluation of multiple sclerosis severity using a new OCT tool.

PURPOSE: To assess the ability of a new posterior pole protocol to detect areas with significant differences in retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) thickness in patients with multiple sclerosis versus healthy control subjects; in addition, to assess the correlation between RNFL and GCL thickness, disease duration, and the Expanded Disability Status Scale (EDSS). METHODS: We analyzed 66 eyes of healthy control subjects and 100 eyes of remitting-relapsing multiple sclerosis (RR-MS) patients. Double analysis based on first clinical symptom onset (CSO) and conversion to clinically definite MS (CDMS) was performed. The RR-MS group was divided into subgroups by CSO and CDMS year: CSO-1 (≤ 5 years) and CSO-2 (≥ 6 years), and CDMS-1 (≤ 5 years) and CDMS-2 (≥ 6 years). RESULTS: Significant differences in RNFL and GCL thickness were found between the RR-MS group and the healthy controls and between the CSO and CDMS subgroups and in both layers. Moderate to strong correlations were found between RNFL and GCL thickness and CSO and CDMS. Furthermore, we observed a strong correlation with EDSS 1 year after the OCT examination. CONCLUSIONS: The posterior pole protocol is a useful tool for assessing MS and can reveal differences even in early stages of the disease. RNFL thickness shows a strong correlation with disability status, while GCL thickness correlates better with disease duration.

Diagnosis of multiple sclerosis using optical coherence tomography supported by artificial intelligence.

BACKGROUND: Current procedures for diagnosing multiple sclerosis (MS) present a series of limitations, making it critically important to identify new biomarkers. The aim of the study was to identify new biomarkers for the early diagnosis of MS using spectral-domain optical coherence tomography (OCT) and artificial intelligence. METHODS: Spectral domain OCT was performed on 79 patients with relapsing-remitting multiple sclerosis (RRMS) (disease duration ≤ 2 years, no history of optic neuritis) and on 69 age-matched healthy controls using the posterior pole protocol that incorporates the anatomic Positioning System. Median retinal thickness values in both eyes and inter-eye difference in healthy controls and patients were evaluated by area under the receiver operating characteristic (AUROC) curve analysis in the foveal, parafoveal and perifoveal areas and in the overall area spanned by the three rings. The structures with the greatest discriminant capacity - retinal thickness and inter-eye difference - were used as inputs to a convolutional neural network to assess the diagnostic capability. RESULTS: Analysis of retinal thickness and inter-eye difference in RRMS patients revealed that greatest alteration occurred in the ganglion cell (GCL), inner plexiform (IPL), and inner retinal (IRL) layers. By using the average thickness of the GCL (AUROC = 0.82) and the inter-eye difference in the IPL (AUROC = 0.71) as inputs to a two-layer convolutional neural network, automatic diagnosis attained accuracy = 0.87, sensitivity = 0.82, and specificity = 0.92. CONCLUSION: This study adds weight to the argument that neuroretinal structure analysis could be incorporated into the diagnostic criteria for MS.

Assessment of visual function and the neuroretina in subjects diagnosed with congenital anomaly of color vision.

This cross-sectional and observational study includes 50 eyes of subjects with color blindness and 50 eyes of control subjects. Visual function (visual acuity, contrast sensitivity, and color vision) and neuroretinal structure were assessed in all subjects using optical coherence tomography (OCT). Significant thinning of the retinal nerve fiber layer, ganglion cell layer, and retina were observed in the color blindness group. Significant thinning was also recorded in layers that involve photoreceptor nuclei (between the outer limiting layer and the Bruch membrane and between the outer plexiform layer and the outer limiting membrane). OCT evaluation based on retinal segmentation is a rapid (5-10 minutes) non-invasive technique and seems to be a good biomarker of color blindness.

Ability of Swept-source OCT and OCT-angiography to detect neuroretinal and vasculature changes in patients with Parkinson disease and essential tremor.

BACKGROUND/OBJECTIVES: To evaluate the ability of swept-source optical coherence tomography (SS-OCT) implemented with angiography analysis (SS-OCTA) to detect neuro-retinal and vasculature changes in patients with Parkinson's disease (PD) and essential tremor (ET), and to distinguish between both pathologies. SUBJECTS/METHODS: A total 42 PD and 26 ET patients and 146 controls underwent retinal evaluation using SS-OCT plus OCT-Angio™. The macular (m) and peripapillary (p) retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL), and macular vasculature were assessed. A Linear discriminant function (LDF) was calculated to evaluate the diagnostic ability of SS-OCTA in both PD and ET. RESULTS: PD patients presented a reduction in mRNFL (p < 0.005), mGCL (all sectors, p < 0.05) and pRNFL (p < 0.005) vs healthy controls, and in mRNFL and pRNFL vs ET patients (p < 0.001). ET patients showed a significant reduction in mGCL vs controls (p < 0.001). No differences were observed in the macular vasculature between groups. Predictive diagnostic variables were significant only for PD and a LDF was obtained with an area under the ROC curve of 0.796. CONCLUSIONS: Neuro-retinal thinning is present in both diseases, being greater in PD. While SS-OCT could be useful in diagnosing ET and PD, the diagnostic potential for SS-OCTA based on an LDF applies only to PD, not ET.

Diagnostic Ability and Capacity of Optical Coherence Tomography-Angiography to Detect Retinal and Vascular Changes in Patients with Fibromyalgia.

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 (p=0.006). Spectralis OCT measurements showed decreased pRNFL in patients in the following sectors: superonasal, p=0.001; nasal, p=0.001; inferonasal, p=0.006; temporal, p=0.001; and inferotemporal, p=0.001. 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, p=0.022). 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.

Evaluation of Progressive Retinal Degeneration in Bipolar Disorder Patients over a Period of 5 Years.

PURPOSE: To quantify visual and retinal changes in patients with bipolar disorder (BD) over 5 years, compared with controls. METHODS: Thirty-eight patients with BD and 122 healthy subjects underwent visual acuity (VA) evaluation, contrast sensitivity vision testing (CSV) with the Pelli Robson and CSV 1000E tests, and retinal thicknesses measurement [ganglion cell layer (GCL) and retinal nerve fiber layer (RNFL)] using Spectralis Optical Coherence Tomography (OCT). All subjects were re-evaluated after 5 years. The relationship between progressive structural changes and disease duration was analyzed. RESULTS: Visual function parameters in BD patients remained unchanged during the follow-up period. A progressive decrease affecting macular and peripapillary RNFL thickness (p < 0.050) was observed in patients. Progressive changes in BD were more pronounced when compared with healthy controls (p < 0.050). A significant correlation between GCL thickness changes and disease duration was found (GCL outer temporal, r = -0.680, p = 0.016; GCL central, r = -0.540, p = 0.038). CONCLUSIONS: Progressive axonal loss was detected in BD patients. Visual function parameters were not affected after the 5-year follow-up. Despite observed changes in the neuroretina of patients with BD, axonal degeneration in these patients seemed to be mild and might be slowed down by other factors, such as BD treatments.

Progressive Functional and Neuroretinal Affectation in Patients With Multiple Sclerosis Treated With Fingolimod.

BACKGROUND: To evaluate the effect of fingolimod in visual function and neuroretinal structures in patients with multiple sclerosis (MS) for a period of 1 year. METHODS: This longitudinal and observational cohort study included 78 eyes of 78 patients with MS treated with fingolimod. All subjects were evaluated every 3 months during 12 months and compared with 32 patients treated with interferon beta. All patients were examined for high-contrast and low-contrast (2.5% and 1.25%) visual acuity (VA), contrast sensitivity vision (CSV) (using Pelli-Robson and CSV-1000E tests), color vision (Farnsworth D-15 and L'Anthony D-15 desaturated tests), and retinal structural measurements (retinal nerve fiber layer [RNFL] and ganglion cell layer [GCL] thickness) using optical coherence tomography (OCT) technology. RESULTS: Patients with MS treated with fingolimod for a period of 1 year showed significant reduction in 100% and 1.25% contrast VA (P = 0.009 and 0.008, respectively), an alteration of contrast sensitivity and color perception (Pelli-Robson test, CSV-1000E test, Farnsworth D-15 desaturated test, and L'Anthony D-15 desaturated test; P < 0.001), GCL thickness reduction (P = 0.007), and an average macular central thickness increase of 2.6 µm (P = 0.006). Patients with MS treated with interferon beta did not show significant changes in visual function tests neither in macular thickness measurements, but they showed a significant reduction in GCL and RNFL thicknesses. The reduction in neuroretinal structures observed by OCT was significantly higher in the interferon-beta group, but patients treated with fingolimod showed a significant increase in macular central thickness and a reduction in low contrast vision (P < 0.001). CONCLUSIONS: Patients with MS treated with fingolimod and with no clinically observable macular edema show a significant change in visual function parameters and average macular central thickness increase compared with those treated with interferon beta. These findings are probably due to subclinical macular edema produced by fingolimod, which might be considered as an indicator for pharmacovigilance of sphingosine-1-phosphate inhibitors to be improved.

Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography.

BACKGROUND: 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). MATERIAL AND METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Ganglion Cell and Retinal Nerve Fiver Layers Correlated with Time Disease of Bipolar Disorder Using 64 Cell Grid OCT Tool.

MATERIALS AND METHODS: Twenty-five eyes of 25 patients with bipolar disorder and 74 eyes of 74 healthy controls underwent retinal measurements of retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) thickness. Measurements were obtained using the Spectralis-OCT device with the new Posterior Pole protocol which assesses the macular area by analyzing retinal thickness in a grid of 64 (8*8) cells. RESULTS: Significant differences (p < 0.05) in RNFL and GCL thickness were found between BD patients and healthy controls, in parafoveal and perifoveal cells respectively. Significant inverse correlations were found between RNFL and GCL thinning at their thickest location and the duration of bipolar disorder. Several predictive variables were observed with a binary logistic regression for the presence/absence of BD: cell 1.3 RNFL (p = 0.028) and GCL in cells 7.8 (p = 0.012), 2.7 (p = 0.043) and 1.3 (p = 0.047). CONCLUSION: Posterior Pole OCT protocol is a useful tool to assess changes in the inner retinal layers in bipolar disorder. These observed changes, especially those affecting the GCL, may be associated with disease evolution and may be predictive of the presence of the disease. OCT data could potentially be a useful tool for clinicians to diagnose and monitor BD patients.

Angiography with optical coherence tomography as a biomarker in multiple sclerosis.

PURPOSE: To investigate superficial retinal microvascular plexuses detected by optical coherence tomography angiography (OCT-A) in multiple sclerosis (MS) subjects and compare them with healthy controls. METHODS: A total of 92 eyes from 92 patients with relapsing-remitting MS and 149 control eyes were included in this prospective observational study. OCT-A imaging was performed using Triton Swept-Source OCT (Topcon Corporation, Japan). The vessel density (VD) percentage in the superficial retinal plexus and optic disc area (6 x 6 mm grid) was measured and compared between groups. RESULTS: MS patients showed a significant decrease VD in the superior (p = 0.005), nasal (p = 0.029) and inferior (p = 0.040) parafoveal retina compared with healthy subjects. Patients with disease durations of more than 5 years presented lower VD in the superior (p = 0.002), nasal (p = 0.017) and inferior (p = 0.022) parafoveal areas compared with healthy subjects. Patients with past optic neuritis episodes did not show retinal microvasculature alterations, but patients with an EDSS score of less than 3 showed a significant decrease in nasal (p = 0.024) and superior (p = 0.006) perifoveal VD when compared with healthy subjects. CONCLUSIONS: MS produces a decrease in retinal vascularization density in the superficial plexus of the parafoveal retina. Alterations in retinal vascularization observed in MS patients are independent of the presence of optic nerve inflammation. OCT-A has the ability to detect subclinical vascular changes and is a potential biomarker for diagnosing the presence and progression of MS.

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

PURPOSE: To evaluate the physiological changes related with age of all retinal layers thickness measurements in macular and peripapillary areas in healthy eyes. METHODS: Wide protocol scan (with a field of view of 12x9 cm) from Triton SS-OCT instrument (Topcon Corporation, Japan) was performed 463 heathy eyes from 463 healthy controls. This protocol allows to measure the thickness of the following layers: Retina, Retinal nerve fiber layer (RNFL), Ganglion cell layer (GCL +), GCL++ and choroid. In those layers, mean thickness was compared in four groups of ages: Group 1 (71 healthy subjects aged between 20 and 34 years); Group 2 (65 individuals aged 35-49 years), Group 3 (230 healthy controls aged 50-64 years) and Group 4 (97 healthy subjects aged 65-79 years). RESULTS: The most significant thinning of all retinal layers occurs particularly in the transition from group 2 to group 3, especially in temporal superior quadrant at RNFL, GCL++ and retinal layers (p≤0.001), and temporal superior, temporal inferior, and temporal half in choroid layer (p<0.001). Curiously group 2 when compared with group 1 presents a significant thickening of RNFL in temporal superior quadrant (p = 0.001), inferior (p<0.001) and temporal (p = 0.001) halves, and also in nasal half in choroid layer (p = 0.001). CONCLUSIONS: Excepting the RNFL, which shows a thickening until the third decade of life, the rest of the layers seem to have a physiological progressive thinning.

Ability of Swept source OCT technology to detect neurodegeneration in patients with type 2 diabetes mellitus without diabetic retinopathy.

PURPOSE: To evaluate neurodegeneration in patients with type 2 Diabetes Mellitus (DM2) without diabetic retinopathy, and to assess the possible role of chronic systemic ischaemia and disease duration in retinal changes. STUDY DESIGN: Observational cross sectional study. METHODS: Sixty eyes of 60 patients with DM2 without signs of diabetic retinopathy (DR), and 60 eyes of 60 healthy controls underwent retinal (ganglion cell layer (GCL), and retinal nerve fiber layer (RNFL) and choroidal evaluation using Swept source Optical coherence tomography, which allows high quality analysis of the different retinal layers and the choroidal plexus. Comparison between patients with presence/absence of systemic vascular complications and different disease duration time was performed. RESULTS: Macular GCL and RNFL were reduced in patients compared to controls (p < 0.001). In the peripapillary area, a reduction of the RNFL (p < 0.001) was observed in patients with DM2. There were no significant changes observed in the choroidal plexus of these patients. Patients with systemic ischaemia presented significant thinning of the choroid and further reduction of the temporal RNFL (p = 0.014) and GCL (p = 0.016) thickness. The GCL and the choroid were also thinner in patients with longer disease duration. CONCLUSIONS: Patients with early DM2 present retinal neurodegeneration prior to the appearance of clinically observable vascular retinal changes. In these patients chronic systemic ischaemia caused reduction of the choroidal plexus and further damage to the retinal layers, adding new information on systemic chronic ischaemia and retinal neurodegeneration in patients with DM2 without DR.

Reproducibility of retinal and choroidal measurements using swept-source optical coherence tomography in patients with Parkinson's disease / Reprodutibilidade das medições da retina e da coroideia utilizando a tomografia de coerência ótica Swept-Source em pacientes com a doença de Parkinson

ABSTRACT Purpose: To assess the reproducibility of retinal and choroidal measurements in the macular and peripapillary areas using swept-source optical coherence tomography in patients with Parkinson's disease. Methods: A total of 63 eyes of 63 patients with idiopathic Parkinson's disease were evaluated using a three-dimensional protocol of swept-source optical coherence tomography. The following layers were analyzed: full retinal thickness, retinal nerve fiber layer, ganglion cell layer, and choroid. The coefficient of variation was calculated for every measurement. Results: In the macular area, the mean coefficients of variation of retinal thickness, ganglion cell layer + thickness, and choroidal thickness were 0.40%, 0.84%, and 2.09%, respectively. Regarding the peripapillary area, the mean coefficient of variation of the retinal nerve fiber layer thickness was 2.78. The inferior quadrant showed the highest reproducibility (coefficient of variation= 1.62%), whereas the superonasal sector showed the lowest reproducibility (coefficient of variation= 8.76%). Conclusions: Swept-source optical coherence tomography provides highly reproducible measurements of retinal and choroidal thickness in both the macular and peripapillary areas. The reproducibility is higher in measurements of retinal thickness versus choroidal thickness.

RESUMO Objetivo: Avaliar a reprodutibilidade das medições da retina e da coroide nas áreas macular e peripapilar utilizando a tomografia de coerência ótica com fonte de varredura pacientes com doença de Parkinson. Métodos: Um total de 63 olhos de 63 pacientes com doença de Parkinson idiopática foram avaliados usando um protocolo 3D de tomografia de coerência ótica de fonte Triton Swept. Foram analisadas as seguintes camadas: espessura retiniana total, camada de fibras nervosas da retina, camada de células ganglionares e coróide. O coeficiente de variação foi calculado para cada medição. Resultados: Na área macular, os coeficientes médios de variação da espessura da retina, da camada de células ganglionares + espessura e da espessura da coróide foram de 0,40%, 0,84% e 2,09%, respectivamente. Em relação à área peripapilar, o coeficiente médio de variação da espessura da camada de fibras nervosas da retina foi de 2,78%. O quadrante inferior apresentou a maior reprodutibilidade (coeficiente de variação= 1,62%), enquanto o setor superonasal apresentou a menor reprodutibilidade (coeficiente de variação= 8,76%). Conclusões: A tomografia de coerência ótica de fonte Triton Swept fornece medições altamente reprodutíveis da espessura da retina e da coroide nas áreas macular e peripapilar. A reprodutibilidade é maior nas medidas da espessura da retina versus a espessura da coróide.

Reproducibility of retinal and choroidal measurements using swept-source optical coherence tomography in patients with Parkinson's disease.

PURPOSE: To assess the reproducibility of retinal and choroidal measurements in the macular and peripapillary areas using swept-source optical coherence tomography in patients with Parkinson's disease. METHODS: A total of 63 eyes of 63 patients with idiopathic Parkinson's disease were evaluated using a three-dimensional protocol of swept-source optical coherence tomography. The following layers were analyzed: full retinal thickness, retinal nerve fiber layer, ganglion cell layer, and choroid. The coefficient of variation was calculated for every measurement. RESULTS: In the macular area, the mean coefficients of variation of retinal thickness, ganglion cell layer + thickness, and choroidal thickness were 0.40%, 0.84%, and 2.09%, respectively. Regarding the peripapillary area, the mean coefficient of variation of the retinal nerve fiber layer thickness was 2.78. The inferior quadrant showed the highest reproducibility (coefficient of variation= 1.62%), whereas the superonasal sector showed the lowest reproducibility (coefficient of variation= 8.76%). CONCLUSIONS: Swept-source optical coherence tomography provides highly reproducible measurements of retinal and choroidal thickness in both the macular and peripapillary areas. The reproducibility is higher in measurements of retinal thickness versus choroidal thickness.

Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features.

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 AUCCLASSIFIER. 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 AUCCLASSIFIER = 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.

Neurodegeneration in Patients with Type 2 Diabetes Mellitus without Diabetic Retinopathy.

PURPOSE: To evaluate neurodegeneration in patients with type 2 diabetes mellitus (DM2) without diabetic retinopathy and to assess the possible role of systemic vascular complications in retinal changes. METHODS: Sixty eyes of 60 patients with DM2 and without any signs of diabetic retinopathy and 60 eyes of 60 healthy controls underwent retinal evaluation using Spectralis optical coherence tomography. Macular ganglion cell layer (GCL) and retinal nerve fiber layer (RNFL) were evaluated. Peripapillary RNFL thickness was assessed using Glaucoma and Axonal Analytics applications. Comparison between patients with the presence/absence of systemic vascular complications and different disease duration was made. RESULTS: Macular GCL was reduced in patients compared to controls (p < 0.001). Differences in the macular RNFL thickness were only observed in the outer inferior sector (p=0.033). A reduction in the peripapillary RNFL (average, inferior, and inferotemporal thickness, p < 0.05 for all three) was observed in patients using both applications. Patients with chronic systemic vascular complications presented a reduction in the temporal RNFL (p=0.019) compared to patients without complications. The superotemporal RNFL thickness was thinner in patients with longer disease duration. CONCLUSIONS: Patients with type 2 DM without diabetic retinopathy and good metabolic control present neurodegeneration affecting neurons in the macular area and axons in different sectors of the optic disc. Systemic vascular complications contributed to further axonal damage in these patients, suggesting a possible role of subclinical ischaemia to retinal neurodegeneration in type 2 DM.

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Reproducibility and reliability of retinal and optic disc measurements obtained with swept-source optical coherence tomography in a healthy population.

PURPOSE: To analyze the reproducibility of macular and peripapillary thickness measurements, and optic nerve morphometric data obtained with Triton Optical coherence tomography (OCT) in a healthy population. STUDY DESIGN: Observational cross sectional study. MATERIAL AND METHODS: A total of 108 eyes underwent evaluation using the Triton Swept Source-OCT. A wide protocol was used and measurements in each eye were repeated three times. Morphometric data of the optic nerve head, full macular thickness, ganglion cell layer (GCL) and retinal nerve fiber layer thickness (RNFL) were analyzed. For each parameter, the coefficient of variation (COV) and the intra-class (ICC) correlation values were calculated. RESULTS: Measurements were highly reproducible for all morphometric measurements of the optic disc, with a mean COV of 6.36%. Macular full thickness showed good COV and ICC coefficients, with a mean COV value of 1.00%. Macular GCL thickness showed a mean COV value of 3.06%, and ICC higher than 0.787. Peripapillary RNFL thickness showed good COV and ICC coefficients, with a mean COV value of 8.31% and ICC higher than 0.684. The inferotemporal sector showed the lowest ICC (0.685). CONCLUSIONS: Triton OCT presents good reproducibility values in measurements corresponding to retinal parameters, with macular measurements showing the highest reproducibility rates. Peripapillary RNFL measurements should be evaluated with caution.