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.

To analyze the early microvascular retinal changes and oscillatory potentials alterations secondary to diabetic retinal damage, 44 eyes of 22 diabetic patients without and with mild diabetic retinopathy (DR) and 18 eyes of 9 healthy controls were examined. All subjects underwent spectral domain optical coherence tomography (SD-OCT), OCT angiography (OCTA), and electroretinography of oscillatory potentials (OPs). At OCTA, vessel area density (VAD), vessel length fraction (VLF), and fractal dimension (FD) were significantly reduced in the superficial vascular plexus (SVP), VLF and FD in the intermediate capillary plexus (ICP), and FD in the deep capillary plexus (DCP) in the diabetic group compared to the control group. The amplitude (A) of OP2, OP3, OP4 and the sum of OPs were significantly reduced in the diabetic group versus the controls, and the last two parameters were reduced also in patients without DR versus the controls. Moreover, in the diabetic group, a significant direct correlation was found between the A of OP1, OP2, OP3 and sOP and the VLF and FD in the SVP, while a statistically significant inverse correlation was found between the A of OP3 and OP4 and the VDI in the ICP and DCP. The reduced oscillatory potentials suggest a precocious involvement of amacrine cells in diabetic eyes, independently of DR presence, and their correlation with vascular parameters underlines the relevance of the crosstalk between these cells and vascular components in the pathophysiology of this chronic disease.

The disorganization of retinal inner layers (DRIL) is an optical coherence tomography (OCT) biomarker strictly associated with visual outcomes in patients with diabetic macular edema (DME) whose pathophysiology is still unclear. The aim of this study was to characterize in vivo, using retinal imaging and liquid biopsy, DRIL in eyes with DME. This was an observational cross-sectional study. Patients affected by center-involved DME were enrolled. All patients underwent spectral domain optical coherence tomography (SD-OCT) and proteomic analysis of aqueous humor (AH). The presence of DRIL at OCT was analyzed by two masked retinal experts. Fifty-seven biochemical biomarkers were analyzed from AH samples. Nineteen eyes of nineteen DME patients were enrolled. DRIL was present in 10 patients (52.63%). No statistically significant difference was found between DME eyes with and without DRIL, considering the AH concentration of all the analyzed biomarkers except for glial fibrillary acidic protein (GFAP), a biomarker of Müller cells dysfunction (p = 0.02). In conclusion, DRIL, in DME eyes, seems to strictly depend on a major dysfunction of Müller cells, explaining its role not only as imaging biomarker, but also as visual function Müller cells-related parameter.

Background and Objectives Microglia, the resident immune cell of the brain and retina, is widespread activated in the white and gray matter (GM) in multiple sclerosis (MS). The objective of this study is to evaluate the presence and number of hyperreflecting foci (HRF), considered clusters of activated and proliferating retinal microglia, and their association with clinical and radiologic disease parameters in relapsing-remitting MS (RRMS). Methods At baseline, 80 patients with RRMS underwent optical coherence tomography (OCT) and 3T-MRI (including 3-dimensional T1, fluid-attenuated inversion recovery, and double inversion recovery sequences), closed to their disease onset (6.3 ± 5.1 months). These patients were then clinically and radiologically followed up for a mean of 43 months, evaluating the no evidence of disease activity (NEDA) condition, further divided into clinical (cNEDA) and radiologic (rNEDA). Patients with a clinical history or MRI/OCT findings suggestive of optic neuritis (ON) were excluded from the study. Results Compared with healthy controls, the HRF number was significantly higher in the inner nuclear layer (INL) of patients with RRMS (19.55 ± 5.65 vs 13.84 ± 2.57, p < 0.001) and associated with INL volume (β: 1.21, p < 0.001). GM lesion volume significantly correlated with the INL HRF count (p = 0.008). Survival analysis revealed a significant association between INL HRF and both cNEDA (p = 0.017) and rNEDA (p = 0.002). Discussion We found a strong association between retinal microglial proliferation and cortical pathology in RRMS, a finding suggesting a possible underlying common immunopathologic mechanism. Furthermore, microglial activation at baseline was observed to predict subsequent inflammatory events, indicating that HRF might be a candidate prognostic biomarker worthy of further investigation. Classification of Evidence This study provides Class II evidence that in patients with early RRMS but without ON, the number of HRF on OCT of the retinal inner nuclear layer is associated with cNEDA and rNEDA.

Background: Hyperreflective retinal foci (HRF) are small, discrete, hyperreflective elements observed in the retina using optical coherence tomography (OCT). They appear in many retinal diseases and have been linked to disease progression, treatment response, and prognosis. However, their definition and clinical use vary widely, not just between different diseases, but also within a single disorder. Methods: This perspective is based on a review of peer-reviewed studies examining HRF across different retinal diseases. The studies included analyzed HRF morphology, distribution, and clinical relevance using OCT. Particular attention was given to histopathological correlations, disease-specific patterns, and advancements in automated quantification methods. Results: HRF distribution and features vary with disease type and even within the same disease. A variety of descriptions have been proposed with different characteristics in terms of dimensions, reflectivity, location, and association with back shadowing. Automated OCT analysis has enhanced HRF detection, enabling quantitative analysis that may expand their use in clinical practice. However, differences in software and methods can lead to inconsistent results between studies. HRF have been linked to microglial cells and may be defined as neuro-inflammatory cells (Inflammatory, I-HRF), migrating retinal pigment epithelium cells (Pigmentary, P-HRF), blood vessels (Vascular, V-HRF), and deposits of proteinaceous or lipid elements leaking from vessels (Exudative, E-HRF). Conclusions: HRF are emerging as valuable imaging biomarkers in retinal diseases. Four main types have been identified, with different morphological features, pathophysiological origin, and, therefore, different implications in the management of retinal diseases. Advances in imaging and computational analysis are promising for their incorporation into personalized treatment strategies.