Unraveling the Spectrum of Uveitis: Insights from an Epidemiological Study in a National Referral Center in Northern Italy.

PURPOSE: Uveitis embraces a heterogeneous group of vision-threatening inflammatory conditions. Understanding uveitis epidemiology, etiology, and clinical findings is fundamental for a prompt diagnosis and optimal patient management. The aim of the study is to report the epidemiology of uveitis in a national referral center in Northern Italy and investigate the visual prognosis. METHODS: This retrospective study was conducted at Uveitis Service (Ospedale San Raffaele) between June 2016 and May 2023. Demographic data, clinical characteristics, and etiological diagnoses of uveitis patients were collected, and visual prognosis was longitudinally explored. RESULTS: 1105 patients with uveitis were included in the study, while 47 patients presented neoplastic masquerade syndrome and have been excluded. The population had a slight majority of females (M/F ratio = 0.76), mean age was 47 years. 25% presented infectious uveitis, primarily due to herpetic etiology, toxoplasmosis, and tuberculosis. Non-infectious uveitis was the most prevalent diagnosis (38%), with sarcoidosis, HLA-B27-associated uveitis, and Fuch's uveitis as prominent causes. Anatomically, anterior segment was most frequently involved (41%). Significant improvement in visual acuity was observed at follow-up, particularly in patients with infectious uveitis. CONCLUSIONS: Our study sheds light into the epidemiological landscape of uveitis in Northern Italy, reflecting changing patterns due to factors such as migration and changing sexual habits. In particular, higher percentages of syphilis have been observed, compared to other European reports. The distribution of non-infectious uveitis reflects other epidemiological European series. Higher percentages of neoplastic masquerade syndromes support the need of early recognition. Our findings offer precious insights for uveitis epidemiology and daily clinical practice.

Rate and Predictors of Misclassification of Active Diabetic Macular Edema as Detected by an Automated Retinal Image Analysis System.

INTRODUCTION: The aim of this work is to estimate the sensitivity, specificity, and misclassification rate of an automated retinal image analysis system (ARIAS) in diagnosing active diabetic macular edema (DME) and to identify factors associated with true and false positives. METHODS: We conducted a cross-sectional study of prospectively enrolled patients with diabetes mellitus (DM) referred to a tertiary medical retina center for screening or management of DME. All patients underwent two-field fundus photography (macula- and disc-centered) with a true-color confocal camera; images were processed by EyeArt V.2.1.0 (Woodland Hills, CA, USA). Active DME was defined as the presence of intraretinal or subretinal fluid on spectral-domain optical coherence tomography (SD-OCT). Sensitivity and specificity and their 95% confidence intervals (CIs) were calculated. Variables associated with true (i.e., DME labeled as present by ARIAS + fluid on SD-OCT) and false positives (i.e., DME labeled as present by ARIAS + no fluid on SD-OCT) of active DME were explored. RESULTS: A total of 298 eyes were included; 92 eyes (31%) had active DME. ARIAS sensitivity and specificity were 82.61% (95% CI 72.37-89.60) and 84.47% (95% CI 78.34-89.10). The misclassification rate was 16%. Factors associated with true positives included younger age (p = 0.01), shorter DM duration (p = 0.006), presence of hard exudates (p = 0.005), and microaneurysms (p = 0.002). Factors associated with false positives included longer DM duration (p = 0.01), worse diabetic retinopathy severity (p = 0.008), history of inactivated DME (p < 0.001), and presence of hard exudates (p < 0.001), microaneurysms (p < 0.001), or epiretinal membrane (p = 0.06). CONCLUSIONS: The sensitivity of ARIAS was diminished in older patients and those without DME-related fundus lesions, while the specificity was reduced in cases with a history of inactivated DME. ARIAS performed well in screening for naïve DME but is not effective in surveillance inactivated DME.

Assessing Diabetic Retinopathy Staging With AI: A Comparative Analysis Between Pseudocolor and LED Imaging.

Purpose: To compare the diagnostic performance of artificial intelligence (AI)-based diabetic retinopathy (DR) staging system across pseudocolor, simulated white light (SWL), and light-emitting diode (LED) camera imaging modalities. Methods: A cross-sectional investigation involved patients with diabetes undergoing imaging with an iCare DRSplus confocal LED camera and an Optos confocal, ultra-widefield pseudocolor camera, with and without SWL. Macula-centered and optic nerve-centered 45 × 45-degree photographs were processed using EyeArt v2.1. Human graders established the ground truth (GT) for DR severity on dilated fundus exams. Sensitivity and weighted Cohen's weighted kappa (wκ) were calculated. An ordinal generalized linear mixed model identified factors influencing accurate DR staging. Results: The study included 362 eyes from 189 patients. The LED camera excelled in identifying sight-threatening DR stages (sensitivity = 0.83, specificity = 0.95 for proliferative DR) and had the highest agreement with the GT (wκ = 0.71). The addition of SWL to pseudocolor imaging resulted in decreased performance (sensitivity = 0.33, specificity = 0.98 for proliferative DR; wκ = 0.55). Peripheral lesions reduced the likelihood of being staged in the same or higher DR category by 80% (P < 0.001). Conclusions: Pseudocolor and LED cameras, although proficient, demonstrated non-interchangeable performance, with the LED camera exhibiting superior accuracy in identifying advanced DR stages. These findings underscore the importance of implementing AI systems trained for ultra-widefield imaging, considering the impact of peripheral lesions on correct DR staging. Translational Relevance: This study underscores the need for artificial intelligence-based systems specifically trained for ultra-widefield imaging in diabetic retinopathy assessment.

Sectorial Ganglion Cell Complex Thickness as Biomarker of Vision Outcome in Patients With Dominant Optic Atrophy.

Purpose: Dominant optic atrophy (DOA) is an inherited condition caused by autosomal dominant mutations involving the OPA-1 gene. The aim of this study was to assess the relationship between macular ganglion cell and inner plexiform layer (GC-IPL) thickness obtained from structural optical coherence tomography (OCT) and visual outcomes in DOA patients. Methods: The study recruited 33 patients with confirmed OPA-1 heterozygous mutation and DOA. OCT scans were conducted to measure the GC-IPL thickness. The average and sectorial Early Treatment Diabetic Retinopathy Study (ETDRS) charts (six-sector macular analysis to enhance the topographical analysis) centered on the fovea were considered. Several regression analyses were carried out to investigate the associations between OCT metrics and final best-corrected visual acuity (BCVA) as the dependent variable. Results: The mean BCVA was 0.43 ± 0.37 logMAR, and the average macular GC-IPL thickness was 43.65 ± 12.56 µm. All of the GC-IPL sectors were significantly reduced and correlated with BCVA. The univariate linear regression and the multivariate stepwise regression modeling showed that the strongest association with final BCVA was observed with the internal superior GC-IPL thickness. Dividing patients based on BCVA, we found a specific pattern. Specifically, in patients with BCVA ≤ 0.3 logMAR, the external superior and inferior sectors together with the internal superior were more significant; whereas, for BCVA > 0.3 logMAR, the external superior sector and internal superior sector were more significant. Conclusions: The study identified OCT biomarkers associated with visual outcomes in DOA patients. Moreover, we assessed a specific OCT biomarker for DOA progression, ranging from patients in the early stages of disease with more preserved GC-IPL sectorial thickness to advanced stages with severe thinning.