Purpose: To describe the epidemiology, clinical features, and classification of uveitis in a large cohort of Colombian patients. Methods: Data were collected from seven ophthalmological referral centers in the four main cities in Colombia. The study included patients with a confirmed diagnosis of uveitis from January 2010 to December 2022. Information on demographics, ophthalmic examination findings, uveitis classification, and etiology was recorded. Results: The study reviewed 3,404 clinical records of patients with uveitis. The mean age at diagnosis was 41.1 (SD 19.0) years, and 54.2% of the patients were female. Overall, 1,341(39.4%) were infectious, 626 (18.4%) non-infectious, and four masquerade syndromes (0.1%). The most common types of uveitis were unilateral (66.7%), acute (48.3%), and non-granulomatous (83%). Anterior uveitis was the most common anatomical localization (49.5%), followed by posterior uveitis (22.9%), panuveitis (22.3%), and intermediate uveitis (5.2%). A diagnosis was established in 3,252 (95.5%) cases; idiopathic was the most common cause (27.7%), followed by toxoplasmosis (25.3%) and virus-associated uveitis (6.4%). The age group between 30 and 50 exhibited the highest frequency of uveitis. Conclusion: This multicenter study comprehensively describes uveitis characteristics in Colombian patients, providing valuable insights into its demographic and clinical features. The study findings emphasize the need to continue updating the changing patterns of uveitis to improve diagnosis and treatment strategies for diseases associated with intraocular inflammation.

The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnosing, treating, and managing medical conditions with remarkable precision. Uveitis, a diverse group of rare eye conditions characterized by inflammation of the uveal tract, exemplifies the complexities in ophthalmology due to its varied causes, clinical presentations, and responses to treatments. Uveitis, if not managed promptly and effectively, can lead to significant visual impairment. However, its management requires specialized knowledge, which is often lacking, particularly in regions with limited access to care. AI's capabilities in pattern recognition, data analysis, and predictive modelling offer significant potential to revolutionize uveitis management. AI can classify disease outcomes, analyze multimodal imaging data, and identify new therapeutic targets. However, transforming these AI models into clinical applications and meeting patient expectations involves overcoming challenges like acquiring extensive, annotated datasets, ensuring algorithmic transparency, and validating these models in real-world settings. This review delves into the complexities of uveitis and the current AI landscape, discussing the development, opportunities, and challenges of AI from theoretical models to bedside application. It also examines the epidemiology of uveitis, the global shortage of uveitis specialists, and the disease's socioeconomic impacts, underlining the critical need for AI-driven approaches. Furthermore, it explores the integration of AI in diagnostic imaging and future directions in ophthalmology, aiming to highlight emerging trends that could transform management of a patient with uveitis and suggesting collaborative efforts to enhance AI applications in clinical practice.

Purpose: To describe the distribution patterns and clinical characteristics of patients diagnosed with uveitis at a specialized uveitis center in Bogotá, Colombia, from 2013 to 2021 and compare these patterns with the previously reported between 1996 and 2006. Methods: We performed an observational descriptive cross-sectional study systematically reviewing clinical records of patients attending between 2013 and 2021. Data were analyzed and compared with previous reports. Results: Of the 489 patients with uveitis, 310 were females (63.4%). The mean age of onset was 38.7, with a range between 1 and 83 years. Bilateral (52.8%), anterior (45.8%), non-granulomatous (90.8%), and recurrent (47.6%) were the most common types of uveitis found in our population sample. The most common cause of uveitis in this study was idiopathic, followed by toxoplasmosis and HLA-B27 + associated uveitis, which differs from the previous Colombian study where ocular toxoplasmosis was the most frequent cause. This highlights a significant shift from infectious etiologies to more immune-mediated processes as the cause of uveitis in Colombia nowadays. Conclusion: The results of this study provide a comparison between the clinical patterns of presentation of uveitis from 1996 to 2006 and the patterns observed from 2013 to 2021, enhancing awareness about the changing dynamics of uveitis in Colombia to guide a better understanding of the diagnosis, classification, and correlation with other systemic conditions of the disease.

This study aims to describe the incidence of ocular involvement in patients with toxoplasmosis and describe the sociodemographic characteristics by age, sex, and region in Colombia, based on the National Health Registry of data between January 1, 2015, and December 31, 2019. We conducted a cross-sectional study using the Integrated Social Protection Information System database from the Colombian Ministry of Health, the unique official database in the country. We used the International Classification of Diseases for all codes of toxoplasmosis with a specific filter for ocular toxoplasmosis (OT) from 2015 to 2019 to estimate the incidence and the demographic status of the disease in Colombia. During the 5 years of study, the crude unadjusted incidence of OT was 42.02 (Confidence Interval 30.29-56.19) cases in 1,000 patients with toxoplasmosis per year, showing a significant increase of incidence when comparing the year 2019 to the year 2015. There was a predominance of female patients (58% of the cases). The distribution by age shows an increase in cases of the disease in subjects aged 15 to 49 years (65.2%). The geographic analysis showed a higher proportion of cases in the Andean region, followed by the Pacific and the Atlantic regions. This is the first study that determines the epidemiological characteristics of OT based on a National Health database in Colombia, showing a public health problem and evidencing the neediness of solidifying preventive and screening strategies in the Colombian population.