Several aspects of cortical organization are thought to remain plastic into adulthood, allowing cortical sensorimotor maps to be modified continuously by experience. This dynamic nature of cortical circuitry is important for learning, as well as for repair after injury to the nervous system. Electrophysiology studies suggest that adult macaque primary visual cortex (V1) undergoes large-scale reorganization within a few months after retinal lesioning, but this issue has not been conclusively settled. Here we applied the technique of functional magnetic resonance imaging (fMRI) to detect changes in the cortical topography of macaque area V1 after binocular retinal lesions. fMRI allows non-invasive, in vivo, long-term monitoring of cortical activity with a wide field of view, sampling signals from multiple neurons per unit cortical area. We show that, in contrast with previous studies, adult macaque V1 does not approach normal responsivity during 7.5 months of follow-up after retinal lesions, and its topography does not change. Electrophysiology experiments corroborated the fMRI results. This indicates that adult macaque V1 has limited potential for reorganization in the months following retinal injury.

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Deep learning-based systems can achieve a diagnostic performance comparable to physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy. To be useful in clinical practise, it is necessary to have well calibrated measures of the uncertainty with which these systems report their decisions. However, deep neural networks (DNNs) are being often overconfident in their predictions, and are not amenable to a straightforward probabilistic treatment. Here, we describe an intuitive framework based on test-time data augmentation for quantifying the diagnostic uncertainty of a state-of-the-art DNN for diagnosing diabetic retinopathy. We show that the derived measure of uncertainty is well-calibrated and that experienced physicians likewise find cases with uncertain diagnosis difficult to evaluate. This paves the way for an integrated treatment of uncertainty in DNN-based diagnostic systems.

Deep learning-based systems can achieve a diagnostic performance comparable to physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy. To be useful in clinical practise, it is necessary to have well calibrated measures of the uncertainty with which these systems report their decisions. However, deep neural networks (DNNs) are being often overconfident in their predictions, and are not amenable to a straightforward probabilistic treatment. Here, we describe an intuitive framework based on test-time data augmentation for quantifying the diagnostic uncertainty of a state-of-the-art DNN for diagnosing diabetic retinopathy. We show that the derived measure of uncertainty is well-calibrated and that experienced physicians likewise find cases with uncertain diagnosis difficult to evaluate. This paves the way for an integrated treatment of uncertainty in DNN-based diagnostic systems.is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. certified by peer review)

Background Retinal pigment epithelium (RPE) tears after bevacizumab treatment for neovascular age-related macular degeneration accompanied by a pigment epithelial detachment (PED) might be caused by stretching forces on the already weakened RPE. The purpose of this study was to evaluate whether simple measurements of optical coherence tomography (OCT) can predict the individual risk of an RPE tear in preoperative candidates. Methods A retrospective chart review study of 393 consecutive patients with neovascular agerelated macular degeneration evaluated OCT images (Stratus-OCT Zeiss, Jena, Germany). The height of the PED, the central retinal thickness, and the maximum retinal thickness were determined by two independent observers and retrospectively analysed. Results Fifteen patients with an RPE tear had a significant higher PED than the remaining study population. In contrast, no correlation was seen with the central retinal thickness. In a linear regression model, the probability of an RPE tear exponentially increased in dependence of the extent of PED. Conclusion The risk of an RPE tear can be estimated by simple measurement of the height of the PED on OCT.

The broader availability of this technology may enhance the diagnosis and follow-up of AMNR.

for scleral perforation, particularly for trainee surgeons. Ambidexterity can be achieved with practice over a period of time under an operating microscope.Back problems related to posture adopted during surgery are due to bending and leaning over the operating site when standing. This is again minimal with the use of operating microscope, required only during break localization and cryopexy.We strongly believe that using the microscope will result in better trained, more dexterous vitreoretinal surgeons, with less risk of inadvertent scleral perforation during surgery and less prone to back problems in later life.Reference 1 Raman SV, Smith M, Simcock PR. Use of the operating microscope for scleral buckling.