Modern biomedical engineering is characterized by the rapid growth of data volumes that require processing and analysis to support clinical decision-making. Information technology plays a key role in ensuring the high-performance processing of these large datasets, contributing to the increased accuracy and speed of clinical diagnoses, as well as more effective subsequent patient treatment. This article aims to review current approaches and technologies used for biomedical data processing and to rethink the approach to using big data in decision support systems. Special attention is given to machine learning methods that enhance data analysis efficiency. The data processing approach proposed in this article allows for an 10-12% increase in the accuracy of spinal pathology classification, confirming its feasibility in medical practice.
Diabetes can lead to a number of serious complications, in particular, diabetic retinopathy, which occurs in patients with diabetes and can lead to vision loss. In this regard, the development of an information system for the diagnosis of diabetic retinopathy is an important task in the medical field. Such a system can greatly facilitate the diagnostic process and help doctors detect and treat diabetic retinopathy in time. As a result of the conducted research, the urgent task of increasing the accuracy of diagnosis of fundus diseases was solved by using methods of pre-processing images to improve their informative characteristics, statistical analysis and differentiation of pathologies with the help of a decision support system based on neural network technologies. A comparative analysis of the existing methods of diagnosing diabetic retinopathy and other eye diseases was carried out, according to which it is clear that intellectual analysis and pre-processing of the received images of the fundus can significantly improve the results of diagnostics, especially early screening, which is important for preventing severe stages of the disease.
A method of visualization of surface and volumetric caustics of reflected and refracted light on mirror surfaces in volume and on functionally specified surfaces consisting of patches of free forms is proposed. Caustics are surfaces near which the intensity of the light field increases sharply. The visualization method tracks the rays of light in the light space when displaying mirror surfaces. Light rays are projected from light sources onto patches of free-form scenes. The limiting volumes of caustic rays are determined, thereby reducing the number of calculations. Only caustic rays are calculated, which make the main contribution to the resulting image.
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