Background We aimed to define the capability of three different publicly available large language models, Chat Generative Pretrained Transformer (ChatGPT-3.5), ChatGPT-4 and Google Gemini in analysing retinal detachment cases and suggesting the best possible surgical planning. Methods Analysis of 54 retinal detachments records entered into ChatGPT and Gemini’s interfaces. After asking ‘Specify what kind of surgical planning you would suggest and the eventual intraocular tamponade.’ and collecting the given answers, we assessed the level of agreement with the common opinion of three expert vitreoretinal surgeons. Moreover, ChatGPT and Gemini answers were graded 1–5 (from poor to excellent quality), according to the Global Quality Score (GQS). Results After excluding 4 controversial cases, 50 cases were included. Overall, ChatGPT-3.5, ChatGPT-4 and Google Gemini surgical choices agreed with those of vitreoretinal surgeons in 40/50 (80%), 42/50 (84%) and 35/50 (70%) of cases. Google Gemini was not able to respond in five cases. Contingency analysis showed significant differences between ChatGPT-4 and Gemini (p=0.03). ChatGPT’s GQS were 3.9±0.8 and 4.2±0.7 for versions 3.5 and 4, while Gemini scored 3.5±1.1. There was no statistical difference between the two ChatGPTs (p=0.22), while both outperformed Gemini scores (p=0.03 and p=0.002, respectively). The main source of error was endotamponade choice (14% for ChatGPT-3.5 and 4, and 12% for Google Gemini). Only ChatGPT-4 was able to suggest a combined phacovitrectomy approach. Conclusion In conclusion, Google Gemini and ChatGPT evaluated vitreoretinal patients’ records in a coherent manner, showing a good level of agreement with expert surgeons. According to the GQS, ChatGPT’s recommendations were much more accurate and precise.

The ability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2′s) to cause multi-organ ischemia and coronavirus-induced posterior segment eye diseases in mammals gave concern about potential sight-threatening ischemia in post coronavirus disease 2019 patients. The radial peripapillary capillary plexus (RPCP) is a sensitive target due to the important role in the vascular supply of the peripapillary retinal nerve fiber layer (RNFL). Eighty patients one month after SARS-CoV-2 infection and 30 healthy patients were selected to undergo structural OCT (optical coherence tomography) and OCTA (optical coherence tomography angiography) exams. Primary outcome was a difference in RPCP perfusion density (RPCP-PD) and RPCP flow index (RPCP-FI). No significant difference was observed in age, sex, intraocular pressure (IOP) and prevalence of myopia. RPCP-PD was lower in post SARS-CoV-2 patients compared to controls. Within the post-COVID-19 group, patients with systemic arterial hypertension had lower RPCP-FI and age was inversely correlated to both RPCP-FI and RPCP-PD. Patients treated with lopinavir + ritonavir or antiplatelet therapy during admission had lower RPCP-FI and RPCP-PD. RNFL average thickness was linearly correlated to RPCP-FI and RPCP-PD within post-COVID-19 group. Future studies will be needed to address the hypothesis of a microvascular retinal impairment in individuals who recovered from SARS-CoV-2 infection.

Background: Systemic vascular involvement in COVID-19 has been identified in several patients: not only endothelial derangement and increased permeability are reported to be early hallmarks of organ damage in patients with COVID-19 but are also the most important cause of worsening of clinical conditions in severe cases of SARS-CoV-2 infection. There are several reasons to hypothesize that the eye, and the retina in particular, could be a target of organ damage in SARS-CoV-2 infection. Methods: This cohort observational study analyzes OCT angiography and structural OCT of 70 post-COVID-19 patients evaluated at 1-month hospital discharge and 22 healthy control subjects. Primary outcomes were macular vessel density (VD) and vessel perfusion (VP); structural OCT features were evaluated as secondary outcomes. In addition, patients and healthy volunteers were evaluated for best corrected visual acuity, slit lamp photograph, and fundus photo image. Results: VD and VP in 3 × 3 and 6 × 6 mm scans for SCP and DCP showed no significant differences between the groups. Similarly, CMT and GCL did not reveal significant differences between post-COVID-19 and healthy patients. Nine patients (12.9%) featured retinal cotton wool spots and 10 patients had vitreous fibrillary degeneration. The prevalence of epiretinal membrane and macular hole was similar in the two groups. One case of extra papillary focal retinal hemorrhage was reported in the post-COVID-19 group. Conclusions: Macula and perimacular vessel density and perfusion resulted unaltered in mild post-COVID-19 patients at 1-month hospital discharge, suggesting no or minimal retinal vascular involvement by SARS-CoV-2.

Purpose: To predict improvement of best-corrected visual acuity (BCVA) 1 year after pars plana vitrectomy for epiretinal membrane (ERM) using artificial intelligence methods on optical coherence tomography B-scan images. Methods: Four hundred and eleven (411) patients with Stage II ERM were divided in a group improvement (IM) (≥15 ETDRS letters of VA recovery) and a group no improvement (N-IM) (<15 letters) according to 1-year VA improvement after 25-G pars plana vitrectomy with internal limiting membrane peeling. Primary outcome was the creation of a deep learning classifier (DLC) based on optical coherence tomography B-scan images for prediction. Secondary outcome was assessment of the influence of various clinical and imaging predictors on BCVA improvement. Inception-ResNet-V2 was trained using standard augmentation techniques. Testing was performed on an external data set. For secondary outcome, B-scan acquisitions were analyzed by graders both before and after fibrillary change processing enhancement. Results: The overall performance of the DLC showed a sensitivity of 87.3% and a specificity of 86.2%. Regression analysis showed a difference in preoperative images prevalence of ectopic inner foveal layer, foveal detachment, ellipsoid zone interruption, cotton wool sign, unprocessed fibrillary changes (odds ratio = 2.75 [confidence interval: 2.49–2.96]), and processed fibrillary changes (odds ratio = 5.42 [confidence interval: 4.81–6.08]), whereas preoperative BCVA and central macular thickness did not differ between groups. Conclusion: The DLC showed high performances in predicting 1-year visual outcome in ERM surgery patients. Fibrillary changes should also be considered as relevant predictors.

Optical coherence tomography angiography (OCTA) has extensively enhanced our comprehension of eye microcirculation and of its associated diseases. In this narrative review, we explored the key concepts behind OCTA, as well as the most recent evidence in the pathophysiology of age-related macular degeneration (AMD) made possible by OCTA. These recommendations were updated since the publication in 2020, and are targeted for 2023. Importantly, as a future perspective in OCTA technology, we will discuss how artificial intelligence has been applied to OCTA, with a particular emphasis on its application to AMD study.

Purpose: Radial keratotomies (RKs) are responsible for corneal irregularities resulting in biometric errors and lower best-corrected visual acuity (BCVA) due to lower-order and higher-order optical aberrations. The aim of the study was to compare performances of new and old generation formulas in a population of RK patients. Methods: RK patients who underwent phacoemulsification with intraocular lens (IOL) implantation were retrospectively recruited. Inclusion criteria were availability of preoperative and 6-month postoperative BCVA assessment, topography, and tomography. Documented refraction instability, corneal ectasia, and previous ocular surgery except for RK were exclusion criteria. Mean prediction error (ME), mean absolute prediction error (MAE), and incidence of MAE > 0.25D were calculated for SRK-T, Barrett True K, EVO 2.0, Kane, and PEARL-DGS. Results: Twenty-seven patients with a mean baseline BCVA of 0.32 ± 0.18 logMAR and a mean corneal root mean square (RMS) value of 1.59 ± 0.91 μm were included. EVO 2.0, Kane, and PEARL-DGS showed a significantly lower MAE and lower ME compared with all other formulas (P < 0.001 and P < 0.001) and a significant lower incidence of MAE >0.25D (P < 0.001). Significant differences were still detected when using 3-mm mean keratometry for IOL calculation. Conclusions: PEARL-DGS, Kane, and EVO 2.0 formulas show superior accuracy in IOL power calculation compared with SRK-T and Barrett True K in RK patients, with no significant differences between the 3.