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Volume 3
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10.3390/biomedinformatics3010008
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Review

Artificial Intelligence in Bladder Cancer Diagnosis: Current Applications and Future Perspectives

by
Giulio Rossin
1,
Federico Zorzi
1,
Luca Ongaro
1,
Andrea Piasentin
1,
Francesca Vedovo
1,
Giovanni Liguori
1,
Alessandro Zucchi
2,
Alchiede Simonato
3,
Riccardo Bartoletti
2,
Carlo Trombetta
1,
Nicola Pavan
3 and
Francesco Claps
1,*
1
Urology Clinic, Department of Medical, Surgical and Health Sciences, University of Trieste, Cattinara Hospital, Strada di Fiume, 447, 34149 Trieste, Italy
2
Department of Translational Research and New Technologies, University of Pisa, 56126 Pisa, Italy
3
Urology Clinic, Department of Surgical, Oncological and Stomatological Sciences, University of Palermo, 90127 Palermo, Italy
*
Author to whom correspondence should be addressed.
BioMedInformatics 2023, 3(1), 104-114; https://doi.org/10.3390/biomedinformatics3010008
Submission received: 6 January 2023 / Revised: 20 January 2023 / Accepted: 22 January 2023 / Published: 1 February 2023
(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)
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Abstract

Bladder cancer (BCa) is one of the most diagnosed urological malignancies. A timely and accurate diagnosis is crucial at the first assessment as well as at the follow up after curative treatments. Moreover, in the era of precision medicine, proper molecular characterization and pathological evaluation are key drivers of a patient-tailored management. However, currently available diagnostic tools still suffer from significant operator-dependent variability. To fill this gap, physicians have shown a constantly increasing interest towards new resources able to enhance diagnostic performances. In this regard, several reports have highlighted how artificial intelligence (AI) can produce promising results in the BCa field. In this narrative review, we aimed to analyze the most recent literature exploring current experiences and future perspectives on the role of AI in the BCa scenario. We summarized the most recently investigated applications of AI in BCa management, focusing on how this technology could impact physicians’ accuracy in three widespread diagnostic areas: cystoscopy, clinical tumor (cT) staging, and pathological diagnosis. Our results showed the wide potential of AI in BCa, although larger prospective and well-designed trials are pending to draw definitive conclusions allowing AI to be routinely applied to everyday clinical practice.
Keywords: bladder urothelial carcinoma; artificial intelligence; diagnosis; biomarker; machine learning; deep learning bladder urothelial carcinoma; artificial intelligence; diagnosis; biomarker; machine learning; deep learning

Share and Cite

MDPI and ACS Style

Rossin, G.; Zorzi, F.; Ongaro, L.; Piasentin, A.; Vedovo, F.; Liguori, G.; Zucchi, A.; Simonato, A.; Bartoletti, R.; Trombetta, C.; et al. Artificial Intelligence in Bladder Cancer Diagnosis: Current Applications and Future Perspectives. BioMedInformatics 2023, 3, 104-114. https://doi.org/10.3390/biomedinformatics3010008

AMA Style

Rossin G, Zorzi F, Ongaro L, Piasentin A, Vedovo F, Liguori G, Zucchi A, Simonato A, Bartoletti R, Trombetta C, et al. Artificial Intelligence in Bladder Cancer Diagnosis: Current Applications and Future Perspectives. BioMedInformatics. 2023; 3(1):104-114. https://doi.org/10.3390/biomedinformatics3010008

Chicago/Turabian Style

Rossin, Giulio, Federico Zorzi, Luca Ongaro, Andrea Piasentin, Francesca Vedovo, Giovanni Liguori, Alessandro Zucchi, Alchiede Simonato, Riccardo Bartoletti, Carlo Trombetta, and et al. 2023. "Artificial Intelligence in Bladder Cancer Diagnosis: Current Applications and Future Perspectives" BioMedInformatics 3, no. 1: 104-114. https://doi.org/10.3390/biomedinformatics3010008

APA Style

Rossin, G., Zorzi, F., Ongaro, L., Piasentin, A., Vedovo, F., Liguori, G., Zucchi, A., Simonato, A., Bartoletti, R., Trombetta, C., Pavan, N., & Claps, F. (2023). Artificial Intelligence in Bladder Cancer Diagnosis: Current Applications and Future Perspectives. BioMedInformatics, 3(1), 104-114. https://doi.org/10.3390/biomedinformatics3010008

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