E.V. Bogdanov1, S.I. Shchukin2, Mugeb Al harosh3

1–3 Bauman Moscow State Technical University (Moscow, Russia)
1 Evgeniy.bogdanov95@gmail.com, 2 schookin2200@yahoo.com, 3 Alharosh@bmstu.ru

Statement of the problem. Diagnosis and monitoring of mucopolysaccharidoses (MPS), a group of hereditary lysosomal storage diseases, remain challenging due to the variability of clinical manifestations and the need for an integrated approach. Advances in molecular genetic and neuroimaging methods require systematization and analysis of modern approaches, including the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML), to improve the accuracy of diagnosis, especially when assessing brain changes. There is a need to standardize methods and develop new algorithms for quantitative data analysis and assessment of disease dynamics.

Objective. To conduct a comprehensive analysis of modern methods for diagnosing and monitoring MPS, covering laboratory, instrumental and neuroimaging approaches; to consider the use of advanced technologies (ML, AI) to improve the accuracy of detection of pathological changes in the brain; to pay attention to the role of quantitative image analysis, segmentation methods and their application in automated systems; to describe the main biomarkers and problems of data interpretation; to assess the need for an interdisciplinary approach and standardization of methods for the formation of promising areas for the development of diagnostics and monitoring of MPS.

Results. A comprehensive analysis of the methods for diagnostics and monitoring of MPS is presented. Laboratory (urine analysis for GAG, enzyme analysis, molecular genetic analysis) and instrumental methods (X-ray, CT, ultrasound, MRI) are described, with an emphasis on MRI as the "gold standard" for CNS visualization. Universal programs for processing MRI images (SPM, FreeSurfer, 3D Slicer, BrainSuite) and image correction methods (Gaussian, median, Fourier, anisotropic diffusion, wavelet filtering), their advantages and limitations in MPS are considered. The problems of assessing disease progression are discussed, including variability of the course, limited availability of quantitative analysis methods and the inaccuracy of existing segmentation algorithms in the presence of pronounced structural changes. The role of AI and ML in solving these problems is emphasized. The article covers the use of biomarkers and the need to standardize diagnostic approaches. The importance of interdisciplinary interaction (genetics, neurology, radiology, bioinformatics) is substantiated.

Practical significance. The results of the analysis systematize information on modern diagnostic and monitoring capabilities for MPS. A review of advanced technologies, such as AI and image processing methods, informs specialists about the potential for their implementation to improve the accuracy of diagnosis and assess the dynamics of the disease. The identified problems and proposed directions (standardization, development of new algorithms, integration of biomarkers, interdisciplinary approach) can serve as a basis for further research and improvement of clinical practice in managing patients with MPS, contributing to improving their quality of life and the effectiveness of treatment.

Bogdanov E.V., Shchukin S.I., Al harosh Mugeb. Review of modern diagnostics and monitoring of mucopolysaccharidoses. Biomedicine Radioengineering. 2025. V. 28. № 6. P. 91–99. DOI: https://doi.org/10.18127/j15604136-202506-10 (In Russian)

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