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Since the end of 2019, an outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), originating in the Chinese city of Wuhan has spread rapidly worldwide causing thousands of deaths. Coronavirus disease (COVID-19) is supported by SARS-CoV-2 and represents the causative agent of a potentially fatal disease that is of great global public health concern. Italy has been the first European country recording an elevated number of infected forcing the Italian Government to call for total lockdown. The lockdown had the aim to limit the spread of infection through social distancing. The purpose of this study is to analyze how the pandemic has affected the patient's accesses to the Ophthalmological Emergency Department of a tertiary referral center in central-northern Italy, during the lockdown period. The charts of all patients that came to the Emergency Department during the lockdown period (March 10-May 4, 2020) have been retrospectively collected and compared with those in the same period of 2019 and the period from 15 January-9 March 2020. A significant reduction of visits during the lockdown has been observed, compared with those of pre-lockdown period (reduction of 65.4%) and with those of the same period of 2019 (reduction of 74.3%). Particularly, during the lockdown, minor and not urgency visits decreased whereas the undeferrable urgency ones increased. These pieces of evidence could be explained by the fear of patients to be infected; but also revealed patients misuse of emergency services.

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Artificial intelligence (AI) and deep learning (DL)-based systems have gained wide interest in macular disorders, including diabetic macular edema (DME). This paper aims to validate an AI algorithm for identifying and quantifying different major optical coherence tomography (OCT) biomarkers in DME eyes by comparing the algorithm to human expert manual examination. Intraretinal (IRF) and subretinal fluid (SRF) detection and volumes, external limiting-membrane (ELM) and ellipsoid zone (EZ) integrity, and hyperreflective retina foci (HRF) quantification were analyzed. Three-hundred three DME eyes were included. The mean central subfield thickness was 386.5 ± 130.2 µm. IRF was present in all eyes and confirmed by AI software. The agreement (kappa value) (95% confidence interval) for SRF presence and ELM and EZ interruption were 0.831 (0.738–0.924), 0.934 (0.886–0.982), and 0.936 (0.894–0.977), respectively. The accuracy of the automatic quantification of IRF, SRF, ELM, and EZ ranged between 94.7% and 95.7%, while accuracy of quality parameters ranged between 99.0% (OCT layer segmentation) and 100.0% (fovea centering). The Intraclass Correlation Coefficient between clinical and automated HRF count was excellent (0.97). This AI algorithm provides a reliable and reproducible assessment of the most relevant OCT biomarkers in DME. It may allow clinicians to routinely identify and quantify these parameters, offering an objective way of diagnosing and following DME eyes.