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Machine learning applications in detection and diagnosis of urology cancers: a systematic literature review

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Abstract

Deep learning integration in cancer diagnosis enhances accuracy and diagnosis speed which helps clinical decision-making and improves health outcomes. Despite all these benefits in cancer diagnosis, the present AI models in urology cancer diagnosis have not been sufficiently reviewed systematically. This paper reviews the artificial intelligence approaches used in cancer diagnosis, prediction, and treatment of urology cancer. AI models and their applications in urology subspecialties are evaluated and discussed. The Scopus, Microsoft Academic and PubMed/MEDLINE databases were searched in November 2022 using the terms “artificial intelligence”, “neural network”, “machine learning,” or “deep learning” combined with the phrase “urology cancers”. The search was limited to publications published within the previous 20 years to identify cutting-edge deep-learning applications published in English. Irrelevant review articles and publications were eliminated. The included research involves two kinds of research analysis: quantitative and qualitative. 48 articles were included in this survey. 25 studies proposed several approaches for prostate cancers, while 15 were for bladder cancers. 8 studies discussed renal cell carcinoma and kidney cancer. The models presented to detect urology cancers have achieved high detection accuracy (77–95%). Deep learning approaches that use convolutional neural networks have achieved the highest accuracy among other techniques. Although it is still progressing, the development of AI models for urology cancer detection, prediction, and therapy has shown significant promise. Additional research is required to employ more extensive, higher-quality, and more recent datasets to the clinical performance of the proposed AI models in urology cancer applications.

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  1. M. Lubbad and D. Karaboga have contributed equally to this work.

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  1. Department of Computer Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey

    M. Lubbad, D. Karaboga, A. Basturk, B. Akay & U. Nalbantoglu

  2. Department of Computer Engineering, Engineering Faculty, Igdir University, Igdir, Turkey

    I. Pacal

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ML wrote the main manuscript text, DK, AB, and BA prepared Tables 1, 2 and 3, and UN and IP prepared Figs. 2 and 3. All authors reviewed the manuscript.

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Lubbad, M., Karaboga, D., Basturk, A. et al. Machine learning applications in detection and diagnosis of urology cancers: a systematic literature review. Neural Comput & Applic 36, 6355–6379 (2024). https://doi.org/10.1007/s00521-023-09375-2

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