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

被引:27
作者
Ferro, Matteo [1 ]
Falagario, Ugo Giovanni [2 ]
Barone, Biagio [3 ]
Maggi, Martina [4 ]
Crocetto, Felice [5 ]
Busetto, Gian Maria [2 ]
del Giudice, Francesco [4 ]
Terracciano, Daniela [6 ]
Lucarelli, Giuseppe [7 ]
Lasorsa, Francesco [7 ]
Catellani, Michele [8 ]
Brescia, Antonio [1 ]
Mistretta, Francesco Alessandro [1 ,9 ]
Luzzago, Stefano [1 ,9 ]
Piccinelli, Mattia Luca [1 ]
Vartolomei, Mihai Dorin [10 ]
Jereczek-Fossa, Barbara Alicja [9 ,11 ]
Musi, Gennaro [1 ,9 ]
Montanari, Emanuele [12 ,13 ]
de Cobelli, Ottavio [1 ,9 ]
Tataru, Octavian Sabin [14 ]
机构
[1] IEO European Inst Oncol, IRCCS Ist Ricovero & Cura Carattere Sci, Dept Urol, I-20141 Milan, Italy
[2] Univ Foggia, Dept Urol & Organ Transplantat, I-71121 Foggia, Italy
[3] AORN St Anna & San Sebastiano, Dept Surg Sci, Urol Unit, I-81100 Caserta, Italy
[4] Sapienza Univ Rome, Policlin Umberto Hosp 1, Dept Maternal Infant & Urol Sci, I-00161 Rome, Italy
[5] Univ Naples Federico II, Dept Neurosci & Reprod Sci & Odontostomatol, I-80131 Naples, Italy
[6] Univ Naples Federico II, Dept Translat Med Sci, I-80131 Naples, Italy
[7] Univ Bari, Dept Emergency & Organ Transplantat, Urol Androl & Kidney Transplantat Unit, I-70124 Bari, Italy
[8] ASST Papa Giovanni XXIII, Dept Urol, I-24127 Bergamo, Italy
[9] Univ Milan, Dept Oncol & Hemato Oncol, I-20122 Milan, Italy
[10] Med Univ Vienna, Dept Urol, A-1090 Vienna, Austria
[11] IEO European Inst Oncol IRCCS, Div Radiat Oncol, I-20141 Milan, Italy
[12] Fdn IRCCS CaGranda Osped Maggiore Policlin, Dept Urol, I-20122 Milan, Italy
[13] Univ Milan, Dept Clin Sci & Community Hlth, I-20122 Milan, Italy
[14] George Emil Palade Univ Med Pharm Sci & Technol Ta, Dept Simulat Appl Med, Targu Mures 540142, Romania
关键词
artificial intelligence; machine learning; deep learning; diagnosis; bladder cancer; TEXTURE FEATURES; RADIOMICS; DISCRIMINATION; SEGMENTATION; PREDICTION; RESECTION; SYSTEM;
D O I
10.3390/diagnostics13132308
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
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页数:22
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共 103 条
[1]   A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition [J].
Abbaspour, Saedeh ;
Fotouhi, Faranak ;
Sedaghatbaf, Ali ;
Fotouhi, Hossein ;
Vahabi, Maryam ;
Linden, Maria .
SENSORS, 2020, 20 (19) :1-14
[2]   Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey [J].
Aggarwal, Ravi ;
Farag, Soma ;
Martin, Guy ;
Ashrafian, Hutan ;
Darzi, Ara .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2021, 23 (08)
[3]   Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors [J].
Ali, Nairveen ;
Bolenz, Christian ;
Todenhoefer, Tilman ;
Stenzel, Arnulf ;
Deetmar, Peer ;
Kriegmair, Martin ;
Knoll, Thomas ;
Porubsky, Stefan ;
Hartmann, Arndt ;
Popp, Juergen ;
Kriegmair, Maximilian C. ;
Bocklitz, Thomas .
SCIENTIFIC REPORTS, 2021, 11 (01)
[4]   Predictive value of MCM5 (ADXBLADDER) analysis in urine of men evaluated for the initial diagnosis of bladder cancer: A comparative prospective study [J].
Anastasi, Emanuela ;
Maggi, Martina ;
Tartaglione, Sara ;
Angeloni, Antonio ;
Gennarini, Giuseppina ;
Leoncini, Pier Paolo ;
Sperduti, Isabella ;
Di Lascio, Giovanni ;
De Stefano, Virgilio ;
Di Pierro, Giovanni Battista ;
Del Giudice, Francesco ;
Busetto, Gian Maria ;
De Berardinis, Ettore ;
Sciarra, Alessandro .
DIAGNOSTIC CYTOPATHOLOGY, 2020, 48 (11) :1034-1040
[5]   Deep learning based digital cell profiles for risk stratification of urine cytology images [J].
Awan, Ruqayya ;
Benes, Ksenija ;
Azam, Ayesha ;
Song, Tzu-Hsi ;
Shaban, Muhammad ;
Verrill, Clare ;
Tsang, Yee Wah ;
Snead, David ;
Minhas, Fayyaz ;
Rajpoot, Nasir .
CYTOMETRY PART A, 2021, 99 (07) :732-742
[6]   MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons [J].
Bandyk, Mark G. ;
Gopireddy, Dheeraj R. ;
Lall, Chandana ;
Balaji, K. C. ;
Dolz, Jose .
COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 134
[7]   The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database [J].
Benjamens, Stan ;
Dhunnoo, Pranavsingh ;
Mesko, Bertalan .
NPJ DIGITAL MEDICINE, 2020, 3 (01)
[8]   Artificial intelligence: A promising frontier in bladder cancer diagnosis and outcome prediction [J].
Borhani, Soheila ;
Borhani, Reza ;
Kajdacsy-Balla, Andre .
CRITICAL REVIEWS IN ONCOLOGY HEMATOLOGY, 2022, 171
[9]  
britannica, HUM INT COGN CONT TH
[10]   Artificial intelligence in urological oncology: An update and future applications [J].
Brodie, Andrew ;
Dai, Nick ;
Teoh, Jeremy Yuen-Chun ;
Decaestecker, Karel ;
Dasgupta, Prokar ;
Vasdev, Nikhil .
UROLOGIC ONCOLOGY-SEMINARS AND ORIGINAL INVESTIGATIONS, 2021, 39 (07) :379-399