2.3. Urinalysis
Urinalysis is an important step in diagnosing and monitoring a wide range of diseases. The introduction of artificial intelligence (AI) methods in this area has significantly improved the accuracy and speed of result interpretation. Key parameters such as density, protein levels, glucose, ketone bodies, erythrocytes, leukocytes, and others allow early detection of pathologies and characterization of their nature. Traditional laboratory methods require highly qualified specialists to evaluate results, which sometimes creates a risk of subjective errors, especially when processing large volumes of data.
The use of AI enables automation of the analysis process, reduces the likelihood of errors, and increases reproducibility of results. Machine learning methods, such as classification algorithms and neural networks, are successfully applied to training datasets to develop models capable of recognizing pathological deviations with high accuracy.
Modern AI systems can not only assess current urine parameters but also track dynamics of changes, which is important for treatment monitoring and therapy effectiveness evaluation. Another important application area is risk prediction for chronic diseases based on comprehensive analysis of urine data.
The implementation of automated solutions in laboratory practice is highly valuable, allowing rapid processing of high sample volumes and obtaining maximum accuracy in interpretation. Overall, integrating AI into urinalysis opens new opportunities for a personalized approach to diagnosis and treatment and supports the development of preventive strategies.
An important aspect remains ensuring data confidentiality and adherence to ethical standards in the use of automated systems, which fosters trust among patients and the medical community.