## 5 Discussion
Based on the results of this study, algorithms for timely newborn stroke recognition were elaborated. Furthermore, it is planned that to adopt these newly elaborated algorithms and train a fuzzy classifier-based diagnostic model using these algorithms for developing machine learning algorithms for decision-making regarding prompt, time-saving stroke recognition. This is an important step towards a new generation of algorithm formation based on machine learning techniques [18]. The advantage of such an approach to diagnosis and prediction, especially in neurology, when a time-dependent recognition of trigger symptoms is critical, is described in the literature [14; 15; 16; 17; 23; 24]. In the study conducted by [PERSON] et al., relevant results in prognostic modeling in pathological conditions regarding edema of soft tissue with the implementation of vessels and blood clotting abnormalities, realized via a fuzzy classifier, were shown [26]. From the literature data, it can be assumed that the fuzzy classifier is an appropriate method for the future conversion of the developed algorithms into automatized algorithms. The basics for its realization will be implemented based on the fundamental principles of neural network circuits, regarding their potential possibilities for application in biosciences and medicine [27]. It is known that machine learning algorithms have to be trained with high-quality data [18]. The clinical algorithms developed in this study for valid, timely IS detection are therefore promising results for further training the fuzzy classifier with this validated and high-quality data, which is planned as the next step of the study. **Clinical impact of the developed algorithms**In the current research, the solved scientific problem consists in the elucidation of the evolutionary clinical-paraclinical peculiarities of IS in children, which will improve the early diagnosis of the disease and the prophylaxis of neurological disorders in the future. The diagnosis and treatment of strokes can be optimized by applying the diagnostic and therapeutic algorithms elaborated in this study. They involve a combination of clinical, neuroimaging, neurofunctional assessment methods, and laboratory tests. Implementing these algorithms in clinical settings will help recognize and prevent childhood strokes, reduce treatment delays for ischemic stroke, and provide real-time decision support. These algorithms include clinical and paraclinical signs that were not previously considered for stroke diagnosis but are now included in newly developed national protocols as pathognomic signs. They are now considered urgent signs for MRI and CT to facilitate real-time decision support. Analyzing the risk factors for ischemic stroke has also allowed for the development of an algorithm to identify and manage newborns and children at risk for this disease, along with recommendations for its prevention in children. **The generalizability of the results to various populations and healthcare settings**  
The study involved evaluating children and newborns from across the country based on specific criteria and signs that were identified in advance as potential predictors and diagnostic signs of ischemic stroke. The findings of the study can be applied to the general population of Moldova, particularly to children and newborns with ischemic stroke. Additionally, these findings can be useful in various healthcare settings, as three distinct algorithms have been developed for specialized medical care, preclinical medical assistance, and management of relatives' behavior. **Future outlook**  
Furthermore, the developed algorithms will also be included in training machine learning algorithms, as it was shown in a series of studies that the training of machine learning algorithms using neuroimaging findings with a certain aim revealed promising results [15; 16; 17; 28]. The presented algorithms will allow specialists in the field to timely diagnose stroke in children and to correctly apply their diagnostic and treatment methods.