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Review
. 2023 Jul 7;13(13):2308.
doi: 10.3390/diagnostics13132308.

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Matteo Ferro  1 , Ugo Giovanni Falagario  2 , Biagio Barone  3 , Martina Maggi  4 , Felice Crocetto  5 , Gian Maria Busetto  2 , Francesco Del Giudice  4 , Daniela Terracciano  6 , Giuseppe Lucarelli  7 , Francesco Lasorsa  7 , Michele Catellani  8 , Antonio Brescia  1 , Francesco Alessandro Mistretta  1   9 , Stefano Luzzago  1   9 , Mattia Luca Piccinelli  1 , Mihai Dorin Vartolomei  10 , Barbara Alicja Jereczek-Fossa  9   11 , Gennaro Musi  1   9 , Emanuele Montanari  12   13 , Ottavio de Cobelli  1   9 , Octavian Sabin Tataru  14
Affiliations
Review

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Matteo Ferro et al. Diagnostics (Basel). .

Abstract

Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.

Keywords: artificial intelligence; bladder cancer; deep learning; diagnosis; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of artificial intelligence and diagnosis of bladder cancer retrieval studies.
Figure 2
Figure 2
Summarization of machine learning process.
Figure 3
Figure 3
Deep learning workflow in bladder cancer.
See this image and copyright information in PMC

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