Applications of neural networks in urology: a systematic review.
Curr Opin Urol
; 30(6): 788-807, 2020 11.
Article
en En
| MEDLINE
| ID: mdl-32881726
ABSTRACT
PURPOSE OF REVIEW Over the last decade, major advancements in artificial intelligence technology have emerged and revolutionized the extent to which physicians are able to personalize treatment modalities and care for their patients. Artificial intelligence technology aimed at mimicking/simulating human mental processes, such as deep learning artificial neural networks (ANNs), are composed of a collection of individual units known as 'artificial neurons'. These 'neurons', when arranged and interconnected in complex architectural layers, are capable of analyzing the most complex patterns. The aim of this systematic review is to give a comprehensive summary of the contemporary applications of deep learning ANNs in urological medicine. RECENT FINDINGS:
Fifty-five articles were included in this systematic review and each article was assigned an 'intermediate' score based on its overall quality. Of these 55 articles, nine studies were prospective, but no nonrandomized control trials were identified.SUMMARY:
In urological medicine, the application of novel artificial intelligence technologies, particularly ANNs, have been considered to be a promising step in improving physicians' diagnostic capabilities, especially with regards to predicting the aggressiveness and recurrence of various disorders. For benign urological disorders, for example, the use of highly predictive and reliable algorithms could be helpful for the improving diagnoses of male infertility, urinary tract infections, and pediatric malformations. In addition, articles with anecdotal experiences shed light on the potential of artificial intelligence-assisted surgeries, such as with the aid of virtual reality or augmented reality.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
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Redes Neurales de la Computación
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Enfermedades Urogenitales Masculinas
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Enfermedades Urogenitales Femeninas
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
/
Systematic_reviews
Límite:
Female
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Humans
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Male
Idioma:
En
Revista:
Curr Opin Urol
Asunto de la revista:
UROLOGIA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Italia