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.

(1) Background: We aimed to reveal the relationship between the choroid and the outer retina with optical coherence tomography (OCT) in patients with diabetes mellitus (DM) with mild or no diabetic retinopathy (DR) in order to find early biomarkers for progressing retinopathy. (2) Methods: We performed a prospective study including 61 eyes of patients with type 1 or type 2 DM and 36 eyes of healthy controls. All subjects were imaged with Spectralis OCT. The choroid was assesseed using enhanced depth imaging OCT (EDI-OCT). Binarization of subfoveal choroidal images was done with public domain software, ImageJ (version 1.53a; National Institutes of Health, Bethesda, Maryland, USA). (3) Results: Luminal area, stromal area and total choroidal area were significantly decreased in diabetic patients compared to control: 0.23 ± 0.07 vs. 0.28 ± 0.08, p = 0.012; 0.08 ± 0.03 vs. 0.10 ± 0.04, p = 0.026; 0.31 ± 0.09 vs. 0.38 ± 0.11, p = 0.008. The thickness of retinal pigment epithelium (RPE) correlated positively with the choroidal vascularity index (CVI). The correlations between outer nuclear layer (ONL), photoreceptors (PR) and foveal choroidal thickness (FChT) were moderately negative. (4) Conclusion: Thicker RPE and a thinner PR layer may be assigned the role of early biomarkers signaling the conversion time to progressing retinopathy.

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the Western World. Optical coherence tomography (OCT) has revolutionized the diagnosis and follow-up of AMD patients. This review focuses on SD-OCT imaging biomarkers which were identified as predictors for progression in intermediate AMD to late AMD, either geographic atrophy (GA) or choroidal neovascularization (CNV). Structural OCT remains the most compelling modality to study AMD features related to the progression such as drusen characteristics, hyperreflective foci (HRF), reticular pseudo-drusen (RPD), sub-RPE hyper-reflective columns and their impact on retinal layers. Further on, we reviewed articles that attempted to integrate biomarkers that have already proven their involvement in intermediate AMD progression, in their models of artificial intelligence (AI). By combining structural biomarkers with genetic risk and lifestyle the predictive ability becomes more accurate.

Patient compliance in chronic illnesses is essential for disease management. This also applies to age-related macular degeneration (AMD), a chronic acquired retinal degeneration that needs constant monitoring and patient cooperation. Therefore, patients with AMD can benefit by being properly informed about their disease, regardless of the condition’s stage. Information is essential in keeping them compliant with lifestyle changes, regular monitoring, and treatment. Large language models have shown potential in numerous fields, including medicine, with remarkable use cases. In this paper, we wanted to assess the capacity of two large language models (LLMs), ChatGPT4 and PaLM2, to offer advice to questions frequently asked by patients with AMD. After searching on AMD-patient-dedicated websites for frequently asked questions, we curated and selected a number of 143 questions. The questions were then transformed into scenarios that were answered by ChatGPT4, PaLM2, and three ophthalmologists. Afterwards, the answers provided by the two LLMs to a set of 133 questions were evaluated by two ophthalmologists, who graded each answer on a five-point Likert scale. The models were evaluated based on six qualitative criteria: ($C_1$) reflects clinical and scientific consensus, ($C_2$) likelihood of possible harm, ($C_3$) evidence of correct reasoning, ($C_4$) evidence of correct comprehension, ($C_5$) evidence of correct retrieval, and ($C_6$) missing content. Out of 133 questions, ChatGPT4 received a score of five from both reviewers to 118 questions (88.72%) for $C_1$, to 130 (97.74%) for $C_2$, to 131 (98.50%) for $C_3$, to 133 (100%) for $C_4$, to 132 (99.25%) for $C_5$, and to 122 (91.73%) for $C_6$, while PaLM2 to 81 questions (60.90%) for $C_1$, to 114 (85.71%) for $C_2$, to 115 (86.47%) for $C_3$, to 124 (93.23%) for $C_4$, to 113 (84.97%) for $C_5$, and to 93 (69.92%) for $C_6$. Despite the overall high performance, there were answers that are incomplete or inaccurate, and the paper explores the type of errors produced by these LLMs. Our study reveals that ChatGPT4 and PaLM2 are valuable instruments for patient information and education; however, since there are still some limitations to these models, for proper information, they should be used in addition to the advice provided by the physicians.

Background. Numerous studies confirmed the main role of the inner blood-retinal barrier in the development of Diabetic Macular Oedema (DMO). Lately, the focus of research shifted towards the external retinal barrier with potential involvement in the pathogenesis of DMO. Objective. We aim to identify the OCT changes of the external blood-retinal barrier in patients with DMO and to define them as biomarkers with predictive value. Materials and method. We set up retrospectively 3 groups of patients diagnosed with nonproliferative diabetic retinopathy (NPDR) and DMO, proliferative diabetic retinopathy (PDR) and DMO, and controls. We compared the RPE thickness in every quadrant between groups and performed correlations between best-corrected visual acuity (BCVA) and the thickness of the retinal layers. The Social Science Statistics platform was used for statistical tests. Results. The NPDR-DMO group consisted of 18 eyes, the PDR-DMO group consisted of 19 eyes, and the control group included 36 eyes. In the PDR-DMO group, RPE thickness was decreased in almost all quadrants ( ); in the NPDR-DMO group, only the central minimum and central maximum values of the RPE thickness were significantly different from the control group. We did not find any strong correlation between BCVA and the thickness of the retinal layers. Conclusion. The thickness of the RPE layer is an OCT biomarker able to predict the functioning of the outer BRB. Eyes with PDR-DMO exhibited decreased thickness of the RPE layer in almost all quadrants, highlighting the degenerative changes occurring in a hypoxic environment. The thickness of a specific layer could not be identified as a biomarker to correlate significantly with BCVA, most likely because we did not analyze specific morphologic features, such as continuity and reflectivity. The analysis of the RPE thickness could clarify the unexplained decrease of BCVA and predict early the evolution of DR.