As part of international scientific cooperation, the research results obtained at the SI "The Filatov Institute of Eye Diseases and Tissue Therapy of the NAMS of Ukraine" have been implemented into the clinical, scientific, and educational activities of the Department of Ophthalmology and Optometry at the Nicolae Testemițanu State University of Medicine and Pharmacy (Chișinău, Republic of Moldova).

The implementation was carried out in accordance with the current Agreement on the Development of International Scientific Cooperation and formalised by the Implementation Act of Research Results entitled "Neural Network-Supported Predictive Analysis of Apoptotic and Inflammatory Biomarkers in Neovascular Glaucoma."

This research is based on the use of modern artificial intelligence methods, in particular neural network modelling technologies, for the integrated analysis of clinical data, markers of apoptosis, the inflammatory response and microcirculatory changes in patients with secondary neovascular glaucoma. This comprehensive approach enables the creation of highly accurate prognostic models designed to assess the likelihood of treatment effectiveness, determine the risk of disease progression and predict functional outcomes.

The main objective of the implementation is to improve the diagnosis, prediction of the clinical course, and personalisation of treatment for patients with secondary neovascular glaucoma through the integrated analysis of molecular-biological, clinical, and haemodynamic parameters using advanced machine learning algorithms and multivariate statistical analysis methods.

The practical value of the implemented findings lies in the ability to:

• early stratification of patients according to their risk of an unfavourable course of secondary neovascular glaucoma;
• predict the probability of successful medical, laser, and surgical treatment;
• the personalisation of therapeutic approaches based on a predictive model developed using neural network algorithms;
• optimising clinical decision-making by selecting the most effective treatment strategy;
• improving postoperative monitoring algorithms and predict long-term treatment outcomes.

The implementation of the research results will contribute to the advancement of evidence-based and personalised ophthalmology, expand the application of digital technologies and artificial intelligence methods in clinical practice, and the improvement of training for ophthalmologists, residents, postgraduate students and academic staff in the field of modern prognostic methods in ophthalmopathology.

The study findings are of significant importance for the further development of international scientific cooperation, the transfer of innovative medical technologies, and the harmonisation of approaches to the management of patients with severe forms of glaucoma in accordance with the principles of current personalised medicine.