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.

Purpose: To characterize, using clustering analysis, the OCT morphological and clinical phenotypes of diabetic macular edema (DME) in a very large population (>2000 DME eyes) using standardized and validated OCT-based biomarkers. Methods: A cross-sectional study was conducted on OCT scans collected from 2355 eyes of 1688 patients with DME and performed during real-world clinical practice. OCT scans were automatically analyzed by a software able to automatically quantify OCT key biomarkers: intraretinal fluid (IRF), subretinal fluid (SRF), hyperreflective retinal foci (I-HRF), and external limiting membrane (ELM) and ellipsoid zone (EZ) interruption. Clustering analysis was performed using the above-mentioned biomarkers, including the distribution of IRF across the three ETDRS rings. Results: The overall population was predominantly composed of type 2 diabetes patients (89%), with a mean diabetes duration of 15.6 ± 10.7 years and mean best corrected visual acuity (BCVA) of 63 ± 18 ETDRS letters. Multivariate clustering identified four morphological phenotypes with distinct patterns of fluid distribution associated with different I-HRF counts, SRF volume, and percentages of ELM/EZ integrity (p < 0.0001). Conclusions: This large OCT analysis identified distinct morphological subtypes of DME, confirming the clinical relevance of key imaging biomarkers. The distribution and severity of DME features differ among clusters, supporting the importance of OCT-based phenotyping in tailoring treatment strategies and understanding disease evolution.

The ocular surface (OS) microbiome is influenced by various factors and impacts on ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes. The 16S rRNA gene sequencing was performed on OS samples collected from 135 healthy individuals using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles. Nationality, allergy, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis revealed significant differences between Spanish and Italian subjects (p-value < 0.001), with a median Shannon index of 1.05 for Spanish subjects and 0.59 for Italian subjects. Additionally, 8 microbial genera were significantly associated with eyeglass usage. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. The Support Vector Machine (SVM) model for Nationality achieved an accuracy of 100%, with an AUC-ROC score of 1.0, indicating excellent performance in classifying microbial profiles. This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.

Background: The aim of this study was to determine if the rise in new surgical procedures for glaucoma is changing the baseline features of patients. Methods: In this retrospective study, we reviewed the baseline features of patients undergoing their first glaucoma surgery in 2011 and 2021, collecting data regarding intraocular pressure (IOP), visual field (VF) parameters, stage of disease, and the type of surgery. Results: In the study, 455 patients were included in the analysis. From these, 230 eyes had glaucoma surgery performed in 2011 (Group A) and 225 eyes in 2021 (Group B). When considering the baseline features, Group A was older than Group B (72.7 ± 10.7 and 70 ± 12.4 years; p = 0.02, respectively), and showed a significantly more advanced VF mean defect (−16.4 ± 8.8 and −13.8 ± 8.7 dB; p < 0.01, respectively) and a higher IOP (25.9 ± 6.6 and 24.9 ± 7.8 mmHg; p = 0.02, respectively). Overall, severe VF damage at the time of surgery was more frequent in Group A (74.3%) than in Group B (60.8%) (p < 0.01). The overall number of traditional glaucoma surgeries was 211 in 2011, reducing to 94 ten years later, with similar severe pre-operative VF defects. In 2021, minimally invasive bleb surgery (MIBS) represented 58% of all surgeries. Conclusions: In the last ten years, patients receiving glaucoma surgery for the first time were younger, had less severe disease, and a more contained IOP. The baseline feature modifications were probably related to the diffusion of new procedures, especially MIBS, which allowed for treating patients at an earlier stage, reserving traditional procedures for advanced cases.

Pupillary hippus, i.e., the spontaneous bilateral oscillation of pupil diameter under constant illumination, provides a non-invasive window into autonomic and central nervous system dynamics. Despite decades of research, a unified framework linking its spectral features, physiological underpinnings, and analytical complexity is still missing. In this narrative review, we synthesize spectral and nonlinear pupillometry studies to propose a paradigm-aware taxonomy of hippus frequency bands that integrates evidence from autonomic tests and cognitive-arousal paradigms. Across the literature, low-frequency components tend to covary with respiratory/vasomotor autonomic rhythms, while higher-frequency fluctuations and complexity indices are more sensitive to cognitive load, visual fatigue, and pathological states but remain methodologically heterogeneous. Finally, we explore contexts in which hippus-based biomarkers show clinical potential, with a main focus on glaucoma as an emerging translational model, while underscoring the methodological and translational gaps that currently hinder their validation and routine clinical adoption. Physiology, spectral metrics, and complexity-based analyses are integrated to lay the foundations of a coherent framework.

Précis: Ultrasound cyclo plasty (UCP) using high-intensity focused ultrasound is an effective and safe procedure in lowering intraocular pressure (IOP) in patients with glaucoma, even in those with high myopia. Purpose: This study aimed to evaluate the efficacy and safety of UCP in glaucoma patients with high myopia. Materials and Methods: In this retrospective, single-center study, we enrolled 36 eyes divided into 2 groups based on axial length: group A (≥26.00 mm) and group B (<26.00 mm). We collected data about visual acuity, Goldmann applanation tonometry, biomicroscopy, and visual field before the procedure and at 1, 7, 30, 60, 90, 180, and 365 days after the procedure. Results: Mean IOP significantly decreased in both groups after treatment (P<0.001). Mean IOP reduction from baseline to the last visit was 9.8±6.6 mmHg (38.7%) in group A and 9.6±6.3 mmHg (34.8%) in group B (P<0.001). Mean IOP at the last visit was 15.8±4.1 mmHg in the myopic group and 18.1±5.6 mmHg in the non-myopic one. Regarding the number of IOP-lowering eyedrops being taken by our patients, statistically significant differences were found between groups A and B neither at baseline (2.8±0.9 and 2.6±1.0; P=0.568) nor 1 year after the procedure (2.5±1.1 and 2.6±1.1; P=0.762). No major complications occurred. All minor adverse events resolved within a few days. Conclusion: UCP seems to be an effective and well-tolerated strategy to lower IOP in glaucoma patients with high myopia.