Diagnostics (Jul 2023)
Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement
- Matteo Ferro,
- Ugo Giovanni Falagario,
- Biagio Barone,
- Martina Maggi,
- Felice Crocetto,
- Gian Maria Busetto,
- Francesco del Giudice,
- Daniela Terracciano,
- Giuseppe Lucarelli,
- Francesco Lasorsa,
- Michele Catellani,
- Antonio Brescia,
- Francesco Alessandro Mistretta,
- Stefano Luzzago,
- Mattia Luca Piccinelli,
- Mihai Dorin Vartolomei,
- Barbara Alicja Jereczek-Fossa,
- Gennaro Musi,
- Emanuele Montanari,
- Ottavio de Cobelli,
- Octavian Sabin Tataru
Affiliations
- Matteo Ferro
- Department of Urology, IEO—European Institute of Oncology, IRCCS—Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Ugo Giovanni Falagario
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
- Biagio Barone
- Urology Unit, Department of Surgical Sciences, AORN Sant’Anna e San Sebastiano, 81100 Caserta, Italy
- Martina Maggi
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
- Felice Crocetto
- Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
- Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
- Francesco del Giudice
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
- Daniela Terracciano
- Department of Translational Medical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
- Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
- Francesco Lasorsa
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
- Michele Catellani
- Department of Urology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
- Antonio Brescia
- Department of Urology, IEO—European Institute of Oncology, IRCCS—Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Francesco Alessandro Mistretta
- Department of Urology, IEO—European Institute of Oncology, IRCCS—Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Stefano Luzzago
- Department of Urology, IEO—European Institute of Oncology, IRCCS—Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Mattia Luca Piccinelli
- Department of Urology, IEO—European Institute of Oncology, IRCCS—Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Mihai Dorin Vartolomei
- Department of Urology, Medical University of Vienna, 1090 Vienna, Austria
- Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Gennaro Musi
- Department of Urology, IEO—European Institute of Oncology, IRCCS—Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’ Granda—Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Ottavio de Cobelli
- Department of Urology, IEO—European Institute of Oncology, IRCCS—Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Octavian Sabin Tataru
- Department of Simulation Applied in Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mures, 540142 Târgu Mures, Romania
- DOI
- https://doi.org/10.3390/diagnostics13132308
- Journal volume & issue
-
Vol. 13,
no. 13
p. 2308
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
