Aim: To evaluate the visual outcome of penetrating ocular injuries with a retained intraocular foreign body (IOFB) managed by pars plana vitrectomy (PPV) and to describe the risk factors associated with poor visual acuity and retinal detachment (RD) development. Methods: Medical records of 56 patients with IOFB that were removed by PPV over a period of 11 years (1 January 2010–31 December 2020) were reviewed. We extracted the demographic data, initial and final best corrected visual acuity (BCVA) using standard Snellen chart, IOFB characteristics, complications and surgical details. Outcome was evaluated according to the final BCVA: poor <0.1, good 0.1–<0.5 or excellent ≥0.5. Results: The mean age was 36.1 ± 14.1 (range, 16–71) years and the majority of patients were males (55 out of 56, 98.2%). IOFB was retinal in 27 (48.2%) cases and intravitreal in 29 cases (51.8%). IOFB size was ≤3mm in 26 (46.4%) cases and >3mm in 30 (53.6%) cases. Preoperative RD was identified in 12 (21.4%) cases and endophthalmitis in 17 cases (30.4%). IOFBs larger than 3 mm and retinal location were associated with RD development. Poor visual outcome was associated with initial BCVA, retinal location, RD and endophthalmitis. Conclusion: Initial BCVA, retinal foreign body, RD and endophthalmitis were risk factors for poor visual outcome.

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It has recently been shown that the interpretation by partial differential equations (PDEs) of a class of convolutional neural networks (CNNs) supports definition of architectures such as parabolic and hyperbolic networks. These networks have provable properties regarding the stability against the perturbations of the input features. Aiming for robustness, we tackle the problem of detecting changes in chest X-ray images that may be suggestive of COVID-19 with parabolic and hyperbolic CNNs and with domain-specific transfer learning. To this end, we compile public data on patients diagnosed with COVID-19, pneumonia, and tuberculosis, along with normal chest X-ray images. The negative impact of the small number of COVID-19 images is reduced by applying transfer learning in several ways. For the parabolic and hyperbolic networks, we pretrain the networks on normal and pneumonia images and further use the obtained weights as the initializers for the networks to discriminate between COVID-19, pneumonia, tuberculosis, and normal aspects. For DenseNets, we apply transfer learning twice. First, the ImageNet pretrained weights are used to train on the CheXpert dataset, which includes 14 common radiological observations (e.g., lung opacity, cardiomegaly, fracture, support devices). Then, the weights are used to initialize the network which detects COVID-19 and the three other classes. The resulting networks are compared in terms of how well they adapt to the small number of COVID-19 images. According to our quantitative and qualitative analysis, the resulting networks are more reliable compared to those obtained by direct training on the targeted dataset.

The era of artificial intelligence (AI) has revolutionized our daily lives and AI has become a powerful force that is gradually transforming the field of medicine. Ophthalmology sits at the forefront of this transformation thanks to the effortless acquisition of an abundance of imaging modalities. There has been tremendous work in the field of AI for retinal diseases, with age-related macular degeneration being at the top of the most studied conditions. The purpose of the current systematic review was to identify and evaluate, in terms of strengths and limitations, the articles that apply AI to optical coherence tomography (OCT) images in order to predict the future evolution of age-related macular degeneration (AMD) during its natural history and after treatment in terms of OCT morphological structure and visual function. After a thorough search through seven databases up to 1 January 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1800 records were identified. After screening, 48 articles were selected for full-text retrieval and 19 articles were finally included. From these 19 articles, 4 articles concentrated on predicting the anti-VEGF requirement in neovascular AMD (nAMD), 4 articles focused on predicting anti-VEGF efficacy in nAMD patients, 3 articles predicted the conversion from early or intermediate AMD (iAMD) to nAMD, 1 article predicted the conversion from iAMD to geographic atrophy (GA), 1 article predicted the conversion from iAMD to both nAMD and GA, 3 articles predicted the future growth of GA and 3 articles predicted the future outcome for visual acuity (VA) after anti-VEGF treatment in nAMD patients. Since using AI methods to predict future changes in AMD is only in its initial phase, a systematic review provides the opportunity of setting the context of previous work in this area and can present a starting point for future research.