Articles | Open Access | https://doi.org/10.55640/

THE ROLE AND EFFICIENCY OF INFORMATION TECHNOLOGIES IN EARLY DETECTION OF UROLOGICAL DISEASES

Khabibullayeva Iroda Dilshod kizi,Ergashev Nursultan Akhmadillo ugli , Andijan Branch of Kokand University.

Abstract

This article analyzes the role of modern information technologies in the early detection of urological diseases and their effectiveness. The possibilities of automating and effectively organizing urological diagnostic processes through information systems, artificial intelligence, telemedicine and data analysis tools are considered. Also, the possibilities of increasing the effectiveness of treatment through early detection of diseases, preventing disease exacerbations and rational use of medical resources are analyzed. The article proves the importance of information technologies based on the results of scientific research and examples used in practice.

Keywords

Diagnostic technologies, machine learning, telemedicine, data analysis, interactive monitoring.

References

Lee, JH, et al. (2023). DeepLearning–AssistedProstateCancerDiagnosisUsingMultiparametric MRI. Radiology, 308(2), 345-353.

Bulletin, W., et al. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. TheLancetOncology, 21(2), 233–241.

Shah, M., et al. (2022). Predictive modeling of prostate cancer using XGBostand SHAP forexplainability. Computers in Biology and Medicine, 143, 105262.

Nagpal, K., et al. (2019). Developmentandvalidationof a deeplearningalgorithmforimprovingGleasonscoringofprostatecancer. NPJ DigitalMedicine, 2(1), 48.

Zheng, Y., et al. (2021). Bladdercancerrecognitionusingdeepconvolutionalneuralnetworks in cystoscopicimages. Computers in Biology and Medicine, 133, 104408.

Lorenzo, T., et al. (2021). Cost-effectiveness and patient satisfaction with tele-urology services. BMJ Open, 11(5), e045199.

Hollander, J. E., & Carr, B. G. (2020). Virtually Perfect? Telemedicine for COVID-19. NewEnglandJournalofMedicine, 382(18), 1679–1681.

Chen, S., et al. (2022). Development of machine learning models for detection of detrusor overactivity and underactivity using urodynamic parameters. Neurourology and Urodynamics, 41(5), 1129–1137.

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THE ROLE AND EFFICIENCY OF INFORMATION TECHNOLOGIES IN EARLY DETECTION OF UROLOGICAL DISEASES. (2025). International Journal of Medical Sciences, 5(06), 333-338. https://doi.org/10.55640/