The aim of this study is to evaluate the changes related to diabetic retinopathy (DR) (no changes, small or moderate changes) in patients with glaucoma and diabetes using artificial intelligence instruments: Support Vector Machines (SVM) in combination with a powerful optimization algorithm—Differential Evolution (DE). In order to classify the DR changes and to make predictions in various situations, an approach including SVM optimized with DE was applied. The role of the optimizer was to automatically determine the SVM parameters that lead to the lowest classification error. The study was conducted on a sample of 52 patients: particularly, 101 eyes with glaucoma and diabetes mellitus, in the Ophthalmology Clinic I of the “St. Spiridon” Clinical Hospital of Iaşi. The criteria considered in the modelling action were normal or hypertensive open-angle glaucoma, intraocular hypertension and associated diabetes. The patients with other types of glaucoma pseudoexfoliation, pigment, cortisone, neovascular and primitive angle-closure, and those without associated diabetes, were excluded. The assessment of diabetic retinopathy changes were carried out with Volk lens and Fundus Camera Zeiss retinal photography on the dilated pupil, inspecting all quadrants. The criteria for classifying the DR (early treatment diabetic retinopathy study—ETDRS) changes were: without changes (absence of DR), mild forma nonproliferative diabetic retinopathy (the presence of a single micro aneurysm), moderate form (micro aneurysms, hemorrhages in 2–3 quadrants, venous dilatations and soft exudates in a quadrant), severe form (micro aneurysms, hemorrhages in all quadrants, venous dilatation in 2–3 quadrants) and proliferative diabetic retinopathy (disk and retinal neovascularization in different quadrants). Any new clinical element that occurred in subsequent checks, which led to their inclusion in severe nonproliferative or proliferative forms of diabetic retinopathy, was considered to be the result of the progression of diabetic retinopathy. The results obtained were very good; in the testing phase, a 95.23% accuracy has been obtained, only one sample being wrongly classified. The effectiveness of the classification algorithm (SVM), developed in optimal form with DE, and used in predictions of retinal changes related to diabetes, was demonstrated.

Aim The study assesed trabeculectomy survival in advanced open angle glaucoma (OAG). Methods This is a retrospective longitudinal study in advanced OAG undergoing primary trabeculectomy. Clinical and demographic parameters were recorded. Surgical survival (qualified/complete) was calculated by Kaplan–Meier analysis for multiple upper limits of intraocular pressure (IOP) with/without medication (≤21 mmHg, ≤18 mmHg, ≤15 mmHg, ≤12 mmHg); Cox hazard ratio analysis identified parameters influencing survival. Results We included 165 eyes from 165 OAG patients: primary forms (POAG) – 86 eyes and secondary (pseudoexfoliative, SOAG) – 79 eyes; mean follow-up interval was 36.21 ± 13.49 months. Clinical parameters were comparable between sub-groups at baseline, except a higher IOP in SOAG vs POAG (36.6 ± 13.2 vs 32.7 ± 11.1 mmHg, p = 0.04); IOP reduction was similar (SOAG vs POAG) 53.93% vs 56.19%, p = 0.45, yet longer hospitalization (8.47 ± 4.39 (SOAG) vs 6.69 ± 3.01 days (POAG), p=0.03) and more medications (0.65 ± 0.24 vs 0.36 ± 0.16, p = 0.05) were needed to achieve comparable final IOP (16.0 ± 9.1 vs 15.1 ± 7.8 mmHg, p = 0.45). Kaplan Meier survival analysis applied for IOP ≤21 mmHg, ≤18 mmHg, ≤15 mmHg and ≤12 mmHg, revealed complete success in 26.2%, 27.3%, 34.5% and 54.6% eyes, respectively; qualified success was found in 45.7%, 48.6%, 77% and 88.6% eyes, respectively. Multiple medications at baseline diminished survival in all tested models (hazard ratio HR > 1, p<0.05), while 5FU+needling improved survival, mostly if combined with lower IOP regime: HR = 0.15, 95% CI = [0.07 −1.12], p = 0.06, if IOP ≤15 mmHg and HR = 0.09, 95% CI = [0.02–1.25], p = 0.06, if IOP ≤12 mmHg. Conclusion Trabeculectomy in advanced OAG reached very good survival rates (77% and 88.6%) at 36 months postoperative, if IOP could be maintained ≤15 mmHg, respectively ≤12 mmHg with medication and additional needling+5FU maneuvers. Specific factors influencing survival were identified for each success definition.

Background: Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review. Materials and Methods: We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease. Results: Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria. Conclusion: In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient’s access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.

In this paper, various machine learning algorithms were used in order to predict the evolution of open-angle glaucoma (POAG). The datasets were built containing clinical observations and objective measurements made at the Countess of Chester Hospital in the UK and at the “St. Spiridon” Hospital of Iași, Romania. Using these datasets, different classification problems were proposed. The evaluation of glaucoma progression was conducted based on parameters such as VFI (Visual field index), MD (Mean Deviation), PSD (Pattern standard deviation), and RNFL (Retinal Nerve Fiber Layer). As classification tools, the following algorithms were used: Multilayer Perceptron, Random Forest, Random Tree, C4.5, k-Nearest Neighbors, Support Vector Machine, and Non-Nested Generalized Exemplars. The best results, with an accuracy of over 90%, were obtained with Multilayer Perceptron and Random Forest algorithms. The NNGE algorithm also proved very useful in creating a hierarchy of the input values according to their influence (weight) on the considered outputs. On the other hand, the decision tree algorithms gave us insight into the logic used in their classification, which is of practical importance in obtaining additional information regarding the rationale behind a certain rule or decision.