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PurposeTo investigate retinal changes prior to vascular signs in patients with type 2 diabetes without diabetic retinopathy or with mild non proliferative diabetic retinopathy.MethodsA cross-sectional study was performed in three groups: patients without diabetes, patients with type 2 diabetes without diabetic retinopathy, and patients with diabetes with mild diabetic retinopathy. Analysis of retinal layers was performed objectively with the Cirrus Review Software 6.0 (Carl Zeiss Meditec, Dublin, CA, USA). Macular cube scans were analyzed with regard to: the ganglion cell layer + inner plexiform layer analysis, retinal nerve fiber layer thickness, central subfoveal retinal thickness and average macular thickness.ResultsIn total, 102 patients were included in this study, of which 28 (27.4%) were classified into control group, 46 (45.0%) classified as diabetic patients with no diabetic retinopathy and 28 (27.4%) classified as mild diabetic retinopathy. Quantitative analysis with the Cirrus software showed that the mean ganglion cell layer and mean retinal nerve fiber layer were thinner in diabetes without diabetic retinopathy group when compared to controls. ANOVA with Bonferroni post test indicated a statistically significant reduction in average retinal thickness in mild diabetic retinopathy group (P = 0.032) compared to control and reduction in ganglion cell layer in diabetes with no diabetic retinopathy (P = 0.039) and mild diabetic retinopathy (P = 0.003). Also indicated reduction in retinal nerve fiber layer in diabetic without diabetic retinopathy and eyes with mild diabetic retinopathy (P < 0.001), compared to controls.ConclusionsOur study found reduction in thickness of ganglion cell layer and retinal nerve fiber layer in patients with diabetes without diabetic retinopathy, which suggests neuroretinal changes before vascular signs of diabetic retinopathy.

IMPORTANCEAdherence to screening for vision-threatening proliferative sickle cell retinopathy is limited among patients with sickle cell hemoglobinopathy despite guidelines recommending dilated fundus examinations beginning in childhood. An automated algorithm for detecting sea fan neovascularization from ultra-widefield color fundus photographs could expand access to rapid retinal evaluations to identify patients at risk of vision loss from proliferative sickle cell retinopathy.OBJECTIVE To develop a deep learning system for detecting sea fan neovascularization from ultra-widefield color fundus photographs from patients with sickle cell hemoglobinopathy. DESIGN, SETTING, AND PARTICIPANTSIn a cross-sectional study conducted at a single-institution, tertiary academic referral center, deidentified, retrospectively collected, ultra-widefield color fundus photographs from 190 adults with sickle cell hemoglobinopathy were independently graded by 2 masked retinal specialists for presence or absence of sea fan neovascularization. A third masked retinal specialist regraded images with discordant or indeterminate grades. Consensus retinal specialist reference standard grades were used to train a convolutional neural network to classify images for presence or absence of sea fan neovascularization. Participants included nondiabetic adults with sickle cell hemoglobinopathy receiving care from a Wilmer Eye Institute retinal specialist; the patients had received no previous laser or surgical treatment for sickle cell retinopathy and underwent imaging with ultra-widefield color fundus photographs between January 1, 2012, and January 30, 2019.INTERVENTIONS Deidentified ultra-widefield color fundus photographs were retrospectively collected.MAIN OUTCOMES AND MEASURES Sensitivity, specificity, and area under the receiver operating characteristic curve of the convolutional neural network for sea fan detection.RESULTS A total of 1182 images from 190 patients were included. Of the 190 patients, 101 were women (53.2%), and the mean (SD) age at baseline was 36.2 (12.3) years; 119 patients (62.6%) had hemoglobin SS disease and 46 (24.2%) had hemoglobin SC disease. One hundred seventy-nine patients (94.2%) were of Black or African descent. Images with sea fan neovascularization were obtained in 57 patients (30.0%). The convolutional neural network had an area under the curve of 0.988 (95% CI, 0.969-0.999), with sensitivity of 97.4% (95% CI, 86.5%-99.9%) and specificity of 97.0% (95% CI, 93.5%-98.9%) for detecting sea fan neovascularization from ultra-widefield color fundus photographs.CONCLUSIONS AND RELEVANCE This study reports an automated system with high sensitivity and specificity for detecting sea fan neovascularization from ultra-widefield color fundus photographs from patients with sickle cell hemoglobinopathy, with potential applications for improving screening for vision-threatening proliferative sickle cell retinopathy.

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Eye surgery, specifically retinal micro-surgery involves sensory and motor skill that approaches human boundaries and physiological limits for steadiness, accuracy, and the ability to detect the small forces involved. Despite assumptions as to the benefit of robots in surgery and also despite great development effort, numerous challenges to the full development and adoption of robotic assistance in surgical ophthalmology, remain. Historically, the first in-human–robot-assisted retinal surgery occurred nearly 30 years after the first experimental papers on the subject. Similarly, artificial intelligence emerged decades ago and it is only now being more fully realized in ophthalmology. The delay between conception and application has in part been due to the necessary technological advances required to implement new processing strategies. Chief among these has been the better matched processing power of specialty graphics processing units for machine learning. Transcending the classic concept of robots performing repetitive tasks, artificial intelligence and machine learning are related concepts that has proven their abilities to design concepts and solve problems. The implication of such abilities being that future machines may further intrude on the domain of heretofore “human-reserved” tasks. Although the potential of artificial intelligence/machine learning is profound, present marketing promises and hype exceeds its stage of development, analogous to the seventieth century mathematical “boom” with algebra. Nevertheless robotic systems augmented by machine learning may eventually improve robot-assisted retinal surgery and could potentially transform the discipline. This commentary analyzes advances in retinal robotic surgery, its current drawbacks and limitations, and the potential role of artificial intelligence in robotic retinal surgery.

A fundamental challenge in retinal surgery is safely navigating a surgical tool to a desired goal position on the retinal surface while avoiding damage to surrounding tissues, a procedure that typically requires tens-of-microns accuracy. In practice, the surgeon relies on depth-estimation skills to localize the tool-tip with respect to the retina in order to perform the tool-navigation task, which can be prone to human error. To alleviate such uncertainty, prior work has introduced ways to assist the surgeon by estimating the tooltip distance to the retina and providing haptic or auditory feedback. However, automating the tool-navigation task itself remains unsolved and largely unexplored. Such a capability, if reliably automated, could serve as a building block to streamline complex procedures and reduce the chance for tissue damage. Towards this end, we propose to automate the toolnavigation task by learning to mimic expert demonstrations of the task. Specifically, a deep network is trained to imitate expert trajectories toward various locations on the retina based on recorded visual servoing to a given goal specified by the user. The proposed autonomous navigation system is evaluated in simulation and in physical experiments using a silicone eye phantom. We show that the network can reliably navigate a needle surgical tool to various desired locations within 137 µm accuracy in physical experiments and 94 µm in simulation on average, and generalizes well to unseen situations such as in the presence of auxiliary surgical tools, variable eye backgrounds, and brightness conditions.

Objective: The objective of this study is to evaluate post-acute symptoms in patients with confirmed severe and critical coronavirus disease 2019 infections. Methods: We evaluated patients with confirmed severe and critical coronavirus disease 2019 infections. Post-acute symptoms were defined as symptoms persisting 4 weeks after the onset of the symptoms and classified as pulmonary, muscular, hematologic, neuropsychiatric, renal, and dermatological. Results: We recovered data from 565 patients (43.7% female) with a mean age of 61.1 years. In 18.2%, at least one hospital readmission was necessary and 11.1% died. In 62.6%, there was at least one persistent symptom, and 28.8% had more than one. Among associated factors, obesity, intensive care support, and mechanical ventilation were related to persistent symptoms. Conclusion: The most prevalent symptoms were pulmonary and neuropsychiatric sequelae, as reported in previous studies. This finding underscores the severity of the coronavirus disease 2019 infection and the need for follow-up after recovery from the initial illness. Obese patients, those requiring mechanical ventilation, female patients, and increased hospital length are at greater chance of having persistent symptoms.

This pilot study of a neovascularization model using intravitreal injection of VEGF165 in pigmented rabbits showed that both doses of 10 and 20 μg were successful and effective in inducing vascular growth in the retina and anterior segment and can therefore be used for evaluating drug efficacy in future studies.