PURPOSE:To evaluate choroidal thickness in young subjects using Enhanced Depth Imaging Spectral Domain Optical Coherence Tomography (EDI SD-OCT) describing volume differences between all the defined areas of the Early Treatment Diabetic Retinopathy Study (ETDRS). DESIGN:Prospective, clinical study. METHODS:Seventy-nine eyes of 95 healthy, young (23.8±3.2years), adult volunteers were prospectively enrolled. Manual choroidal segmentation on a 25-raster horizontal scan protocol was performed. The measurements of the nine subfields defined by the ETDRS were evaluated. RESULTS:Mean subfoveal choroidal thickness was 345.67±81.80µm and mean total choroidal volume was 8.99±1.88mm 3 . Choroidal thickness and volume were higher at the superior and temporal areas compared to inferior and nasal sectors of the same diameter respectively. Strong correlations between subfoveal choroidal thickness and axial length (AL) and myopic refractive error were obtained, r = -0.649, p<0.001 and r = 0.473, p<0.001 respectively. Emmetropic eyes tended to have thicker subfoveal choroidal thickness (381.94±79.88µm versus 307.04±64.91µm) and higher total choroidal volume than myopic eyes (9.80± 1.87mm3 versus 8.14±1.48mm3). The estimation of the variation of the subfoveal choroidal thickness with the AL was -43.84µm/mm. In the myopic group, the variation of the subfoveal choroidal thickness with the myopic refractive error was -10.45µm/D. CONCLUSIONS:This study establishes for the first time a normal database for choroidal thickness and volume in young adults. Axial length, and myopic ammetropy are highly associated with choroidal parameters in healthy subjects. EDI SD-OCT exhibited a high degree of intraobserver and interobserver repeatability. M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT INTRODUCTIONThe development of optical coherence tomography (OCT) technology has revolutionized the diagnostic, monitoring and therapeutic approaches to many retinal diseases. Spectral domain OCT (SD-OCT) offers improved axial resolution (3 µm); by providing 19,000 A-scans per second, it shortens examination times, reducing the eye exposure as well as artifacts 1 . The latest development in OCT technology, sweptsource longer-wavelength OCT (SS-OCT), has a longer-band light source than does the conventional instrumentation (1 µm band light source), providing higher penetration through the retinal pigment epithelium (RPE) and allowing for better visualization of the choroid; however, at the present time, SS-OCT use is limited to research.The role of the choroid in a number of diseases, including central serous chorioretinopathy, high myopia, age-related macular degeneration, choroidal melanoma, and polypoidal choroidal vasculopathy, emphasizes the importance of understanding choroidal structure in ocular disease [2][3][4][5] . Indocyanine green has been the best tool for studying choroidal vasculature; however, it does not provide a quantitative evaluation of the layer; other imaging methods such as echography aid in evaluating the layer, and MRI ...

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.

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.