Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen’s kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

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IVB resulted in superior long-term functional and anatomical outcomes compared with PDT. In particular, PDT resulted in a greater BCVA decrease and CRA increase compared with IVB in eyes with preoperative diffuse CRA. However, the clinical outcomes were not different in eyes with preoperative tessellated fundi.

In challenging cases of EON, the mGCIPL thickness has better diagnostic performance in detecting early-onset EON as compared with using pRNFL thickness. Among the early detection ability of mGCIPL thickness, minimum GCIPL thickness has high diagnostic ability.

Background/aimsTo evaluate subtypes and characteristics of dry eye (DE) using conventional tests and dynamic tear interferometry, and to investigate determinants of disease severity in each DE subtype.Methods309 patients diagnosed with DE and 69 healthy controls were prospectively enrolled. All eyes were evaluated using Ocular Surface Disease Index (OSDI), Schirmer’s test I (ST1) and Meibomian gland dysfunction (MGD) grade were analysed. The tear interferometric pattern and lipid layer thickness were determined using DR-1α and LipiView II, respectively.ResultsDynamic interferometric analysis revealed 56.6% of patients with DE exhibited Jupiter patterns, indicative of aqueous-deficiency, while 43.4% exhibited crystal patterns, indicative of lipid deficiency. These findings were in accordance with classification based on ST1 scores and MGD grade. Conventional assessment indicated 286 patients exhibited evidence of evaporative DE (EDE) due to MGD, while only 11 exhibited signs of pure aqueous-deficient DE (pure ADDE, only ST1 ≤5 mm). Interestingly, of 286 patients with EDE, 144 were categorised into the mixed-ADDE/EDE group, in which ST1 was identified as a strong negative determinant of OSDI. In contrast, 72.2% of patients with mixed-ADDE/EDE exhibited Jupiter patterns (Jupiter mixed), while 27.8% exhibited crystal patterns (crystal mixed). OSDI values were significantly higher in the crystal-mixed group than in the Jupiter mixed, in which OSDI scores were independently associated with ST1 values only.ConclusionsOur findings indicate that majority of EDE patients also exhibit aqueous deficiency, which can aggravate symptoms even in patients with lipid-deficient mixed-ADDE/EDE. Conventional assessments should be combined with interferometric tear analysis to determine the most appropriate treatment for each DE patient.