Artificial intelligence and neural networks in pediatric urology

Introduction. Artificial intelligence (AI) and neural networks are powerful tools that can analyze large amounts of data, identify patterns, and make forecastings. Ultrasound, radiography, scintigraphy, and the quantitative indices (degrees, sizes, indices) used in the diagnosis of urological diseases in children are an ideal object for training computer vision algorithms for the purpose of automatic analysis and calculation of the indices of interest to the doctor. Various AI models have been proposed for predicting treatment outcomes, risks of complications, and developing personalized therapy. AI can reduce the workload on medical personnel by automating routine tasks. A decision support system, remote patient monitoring capabilities, virtual simulators, chatbots, and assistants are technologies that are in high demand among health workers. However, there is a number of limitations to the use of AI. It is important to remember that algorithms are tested on ideally prepared data sets, while in real practice, doctors are faced with incomplete information, technical artifacts, and atypical cases. Therefore, for the successful integration of AI models into pediatric urology, it is necessary to ensure high quality of data for machine learning, security of collection and storage of personal patient data, and compliance with ethical standards. Aim. To analyze the literature data on the use of AI in pediatric urology in the world and Russia in the diagnosis of vesicoureteral reflux, hydronephrosis, hypospadias, posterior urethral valve.
A systematic search of scientific publications was conducted in the PubMed, Scopus, Google Scholar, eLibrary.ru databases for the period 2018–2024. The sample included clinical studies (n ≥ 50), meta-analyses, and PRISMA methodology was used for systematic reviews. The integration of AI technologies into clinical practice has great potential for solving clinical problems in pediatric urology. The limitations for successful implementation remain the insufficient reliability of existing models and the lack of algorithms adapted for the clinical use.

literature review, artificial neural network, artificial intelligence, machine learning, pediatric urology

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