Background/Objectives: Diabetic retinopathy (DR) develops from the interplay of vascular, inflammatory, and neurodegenerative processes. Citicoline, a natural compound essential for cell membranes, enhances neurotransmitter levels, has a neuroprotective effect, reduces oxidative stress by increasing glutathione, and decreases glutamate toxicity. Studies suggest that a citicoline liposomal formulation (eye drops) may prevent diabetes-induced retinal neurodegeneration. This study aimed to evaluate the impact of citicoline eye drops on the clinical signs of DR in clinical settings. Methods: More than 100 patients with nonproliferative DR (NPDR) were selected consecutively from the DR screening program and included in this real-life prospective observational clinical study. Each patient underwent color-fundus photography of two fields (macular field and disk/nasal field) in both eyes using a standard 45° fundus camera. Patients were prescribed citicoline eye drops and followed for a period of one year or longer. Results: In 4 patients with NPDR and macular hard exudates, the application of citicoline (Omk1^®) eye drops three times a day for at least six months to a year resulted in a reduction or complete disappearance of hard exudates. Conclusions: Our study, to the best of our knowledge, is the first one that establishes a clinically positive effect of citicoline eye drops on hard exudates in DR. However, to support the potential value of citicoline in the treatment of DR, the conclusions of this study still need to be confirmed by statistical analysis of a larger sample size and prospective studies with longer follow-up periods.

Background/Objectives: We aimed to evaluate the diagnostic accuracy of the MONA.health artificial intelligence (AI) software (Version 1.0.0; MONA.health, Leuven, Belgium) and compare its advantages in screening for diabetic retinopathy (DR) and diabetic macular edema (DME) with standard fundus photography. Methods: This cross-sectional, real-life instrument validation study was conducted at the Vuk Vrhovac University Clinic in Zagreb during routine DR screening and included 296 patients (592 eyes) with diabetes. Following standard fundus photography using a 45° Zeiss VISUCAM NM/FA camera (Carl Zeiss Meditec AG, Jena, Germany), each patient also underwent imaging with an automated portable retinal camera (NFC-600, Crystalvue Ophthalmic Instruments, Taoyuan City, Taiwan). Two retina specialists independently graded images from the standard camera, while images from the NFC-600 were analyzed using the MONA.health AI software. Results: Among the 592 eyes, human grading identified 81 with any DR, including 17 with mild NPDR, 64 with referable DR (moderate/severe NPDR or PDR), and 13 with DME. The MONA.health AI software identified 65 eyes with referable DR and 19 with DME. For MONA DR screening compared to the standard fundus camera, the area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, kappa agreement, diagnostic odds ratio, and diagnostic effectiveness were 99.74%, 100%, 99.81%, 99.33%, 100%, 528.00, 0.00, 0.99, infinity, and 99.85%, respectively. For MONA DME screening, these metrics were 97.97%, 100%, 98.95%, 85.93%, 100%, 95.67, 0.00, 0.81, infinity, and 99.02%, respectively. The MONA AI screening process required 1 day of training and approximately 5 min for image capture and analysis, compared to 7 days of training and 13 min for image acquisition and grading with the standard method. Conclusions: These findings demonstrate that the MONA.health AI software matches the accuracy of standard fundus photography for screening and early detection of referable DR and DME, while offering a faster, simpler, and more user-friendly workflow that significantly reduces the time to obtain screening results.

Diabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults. Systematic screening programmes in the United Kingdom, Ireland, and the Nordic countries have reduced diabetes-related visual loss, yet many European countries, including Croatia, lack organised screening. This narrative review examines European DR screening programmes, evaluates telemedicine and artificial intelligence (AI) as enabling technologies, and proposes a phased roadmap for a national programme in Croatia. Croatia has nearly 400,000 registered persons with diabetes, a national diabetes registry (CroDiab) linked to the Central Health Information System (CEZIH), and pilot screening data showing 40% DR prevalence among screened patients with type 2 diabetes. The proposed programme combines decentralised telemedicine-based imaging at primary care sites with centralised grading, a staged rollout across three phases targeting 400,000 annual screenings, and stepwise AI integration for triage. Successful European programmes share standardised digital imaging, trained grading workforces, embedded quality assurance, and registry linkage. With dedicated funding and sustained political commitment, Croatia could adopt a hybrid telemedicine–AI model and, under favourable implementation conditions, reduce the burden of advanced DR over the coming decade.