With the growing significance of artificial intelligence in healthcare, new perspectives are emerging in primary care. Diabetic retinopathy, a microvascular complication of diabetes mellitus, often remains unnoticed until patient is facing complications. Artificial intelligence presents a promising solution that can enhance the accessibility of diabetic retinopathy screening for a broader range of patients. The key challenge lies in successfully integrating the solution into clinical practice, a demanding process with multiple phases to ensure the resulting medical device is effective and safe for patient use. Aireen software uses artificial intelligence to perform diabetic retinopathy screening on retinal images captured by optical fundus cameras. The medical device complies with European Medical Device Regulation 2017/745 and was introduced to the market in 2023. Collaboration between physicians and the development team played a crucial role throughout the entire lifecycle of the medical device. Physicians were engaged in defining the intended use of the medical device, risk analysis, data annotation for training and software validation, as well as throughout a clinical trial. A clinical trial was conducted on 1,274 patients with type 1 and type 2 diabetes mellitus, where Aireen medical device achieved a sensitivity of 94.0% and a specificity of 90.7% compared to the reference evaluation. This clinical trial confirmed the potential of Aireen to enhance the availability of diabetic retinopathy screening and early disease detection.

• Klíčová slova
• diabetic retinopathy, screening, artificial intelligence,
• MeSH
• diabetická retinopatie * diagnóza   MeSH
• lidé MeSH
• plošný screening metody   přístrojové vybavení   MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

OBJECTIVE: The aim of this comprehensive paper is to acquaint the readers with evaluation of the retinal images using the arteficial intelligence (AI). Main focus of the paper is diabetic retinophaty (DR) screening. The basic principles of the artificial intelligence and algorithms that are already used in clinical practice or are shortly before approval will be described. METHODOLOGY: Describing the basic characteristics and mechanisms of different approaches to the use of AI and subsequently literary minireview clarifying the current state of knowledge in the area. RESULTS: Modern systems for screening diabetic retinopathy using deep neural networks achieve a sensitivity and specificity of over 80 % in most published studies. The results of specific studies vary depending on the definition of the gold standard, number of images tested and on the evaluated parameters. CONCLUSION: Evaluation of images using AI will speed up and streamline the diagnosis of DR. The use of AI will allow to keep the quality of the eye care at least on the same level despite the raising number of the patients with diabetes.

• Klíčová slova
• Diabetic retinopathy, artificial intelligence, diabetic retinopathy, mass screening, screening,
• MeSH
• algoritmy MeSH
• diabetes mellitus * MeSH
• diabetická retinopatie * diagnóza   MeSH
• inteligence MeSH
• lidé MeSH
• plošný screening MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of severe visual loss in the working population. Therefore, both DR and DME have a significant socioeconomic and health impact, which taking into account the epidemiologic predictions is expected to increase. A crucial role in the management of DR and DME (not only for individuals, but also for the population) is played by an adequate screening program. This is based on the structure and organization of the healthcare system, the latest scientific developments in diagnostics (imaging) as well as technological advancements in computing (artificial intelligence, telemedicine) and their practical use. The recommendation presented by World Health Organization is also important. This paper evaluates all these factors, including evidence-based medicine reports and experience from existing DR and DME screening programs in comparable countries. Based on an evaluation of these parameters, recommended guidelines have been formulated for screening for DR and DME in the Czech Republic, including linkage to the Czech National Screening Center and the organization of the healthcare system.

• Klíčová slova
• Diabetic retinopathy, diabetic macular edema, diabetic retinopathy, guidelines, mass screening, recommended guidelines, screening,
• MeSH
• diabetes mellitus * MeSH
• diabetická retinopatie * diagnóza   MeSH
• lidé MeSH
• makulární edém * diagnóza   etiologie   MeSH
• optická koherentní tomografie škodlivé účinky   metody   MeSH
• telemedicína * metody   MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990-2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep-wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population-all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA.

• Klíčová slova
• Artificial intelligence, Behavioural patterns, Cytokine storm (COVID-19), Diabetes mellitus, Diabetic retinopathy, Expert recommendations, Flammer syndrome, Health policy, Health risk assessment, Health-to-disease transition, Healthcare economy, Individualised patient profile, Inflammation, Ischemic stroke, Mitochondrial health, Mitophagy, Patient-friendly non-invasive approach, Population screening, Predictive preventive personalised medicine (PPPM / 3PM), Primary and secondary care, Sleep medicine, Suboptimal health, Sudden cardiac arrest/death, Tear fluid analysis, Viromics and metabolomics,
• Publikační typ
• časopisecké články MeSH