Improving the effectiveness of screening for chronic noncommunicable diseases using artificial intelligence-based technologies

Background. According to various data, the prevalence of chronic non-communicable diseases among young people tends to increase. This is often due to lifestyle changes, increased stress factors, poor nutrition, low physical activity, bad habits, etc. It is known that the pathological effect of the main risk factors and the formation of diseases begins in adolescence and young age, and therefore, the development of a concept of their prevention is of particular interest for this particular population group. In this regard, early detection and diagnosis of chronic noncommunicable diseases play an important role in preventing their progression, improving the prognosis and quality of life of young patients. Identification of risk factors and determination of their severity at an early stage of the development of diseases allows them to begin their timely correction, which contributes to the prevention of complications. However, low health awareness and lack of medical literacy among this population group is an obstacle to the early detection of chronic noncommunicable diseases in young people. One of the key tools for early detection of chronic noncommunicable diseases in young people is screening aimed at identifying risk factors and primary signs of the disease in people without clinical manifestations. Screening can be carried out using various methods, including questionnaires. The introduction of automated screening diagnostic systems using artificial intelligence is of genuine interest among young people. Moreover, artificial intelligence technologies actively contribute to the creation of conditions for improving the quality of health services.

Results. We have developed and tested a methodology for remote multidisciplinary questionnaire screening of chronic noncommunicable diseases for the first stage of medical examination of young people. The system has allocated a contingent of subjects with high, medium and low risk, and also helps to collect a preliminary medical history for each subject, which helps to improve the quality of medical decision-making and reduces its subjective component, thereby increasing the time for direct examination of the patient. Each subject received personalized medical recommendations on a healthy lifestyle, taking into account the identified risk factors and their severity.

Conclusion. The present development of St. Petersburg programmers and doctors makes it possible to optimize the provision of medical and preventive care to the population and improve the quality of patient examination.

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