Purpose To ascertain the anatomic factors that help achieve non-surgical sealing in full thickness macular hole (FTMH). Methods Retrospective collaborative study of FTMH that closed without surgical intervention. Results A total of 78 patients (mean age 57.9 years) included 18 patients with blunt ocular trauma, 18 patients that received topical or intravitreal therapies and 42 patients with idiopathic FTMH. Mean±SD of the initial corrected visual acuity (VA) in logMAR improved from 0.65±0.54 to 0.34±0.45 (p<0.001) at a mean follow-up of 33.8±37.1 months. FTMH reopened in seven eyes (9.0%) after a mean of 8.6 months. Vitreomacular traction was noted in 12 eyes (15.8%), perifoveal posterior vitreous detachment in 42 (53.8%), foveal epiretinal membrane in 10 (12.8%), cystoid macular oedema (CME) in 49 (62.8%) and subretinal fluid (SRF) in 20 (25.6%). By multivariate analysis, initial VA correlated to the height (p<0.001) and narrowest diameter of the hole (p<0.001) while final VA correlated to the basal diameter (p<0.001). Time for closure of FTMH (median 2.8 months) correlated to the narrowest diameter (p<0.001) and the presence of SRF (p=0.001). Mean time for closure (in months) was 1.6 for eyes with trauma, 4.3 for eyes without trauma but with therapy for CME, 4.4 for eyes without trauma and without therapy in less than 200 µm in size and 24.7 for more than 200 µm. Conclusion Our data suggest an observation period in new onset FTMH for non-surgical closure, in the setting of trauma, treatment of CME and size <200 µm.

Age-related macular degeneration is the leading cause of vision loss in developed countries, and wet-type AMD requires urgent treatment and rapid diagnosis because it causes rapid irreversible vision loss. Currently, AMD diagnosis is mainly carried out using images obtained by optical coherence tomography. This diagnostic process is performed by human clinicians, so human error may occur in some cases. Therefore, fully automatic methodologies are highly desirable adding a layer of robustness to the diagnosis. In this work, a novel computer-aided diagnosis and visualization methodology is proposed for the rapid identification and visualization of wet AMD. We adapted a convolutional neural network for segmentation of a similar domain of medical images to the problem of wet AMD segmentation, taking advantage of transfer learning, which allows us to work with and exploit a reduced number of samples. We generate a 3D intuitive visualization where the existence, position and severity of the fluid were represented in a clear and intuitive way to facilitate the analysis of the clinicians. The 3D visualization is robust and accurate, obtaining satisfactory 0.949 and 0.960 Dice coefficients in the different evaluated OCT cube configurations, allowing to quickly assess the presence and extension of the fluid associated to wet AMD.

OCT is a non-invasive imaging technique commonly used to obtain 3D volumes of the ocular structure. These volumes allow the monitoring of ocular and systemic diseases through the observation of subtle changes in the different structures present in the eye. In order to observe these changes it is essential that the OCT volumes have a high resolution in all axes, but unfortunately there is an inverse relationship between the quality of the OCT images and the number of slices of the cube. This results in routine clinical examinations using cubes that generally contain high-resolution images with few slices. This lack of slices complicates the monitoring of changes in the retina hindering the diagnostic process and reducing the effectiveness of 3D visualizations. Therefore, increasing the cross-sectional resolution of OCT cubes would improve the visualization of these changes aiding the clinician in the diagnostic process. In this work we present a novel fully automatic methodology to perform the synthesis of intermediate slices of OCT image volumes in an unsupervised manner. To perform this synthesis, we propose a fully convolutional neural network architecture that uses information from two adjacent slices to generate the intermediate synthetic slice. We also propose a training methodology, where we use three adjacent slices to train the network by contrastive learning and image reconstruction. We test our methodology with three different types of OCT volumes commonly used in the clinical setting and validate the quality of the synthetic slices created with several medical experts and using an expert system.