Literature DB >> 31092914

A new era: artificial intelligence and machine learning in prostate cancer.

S Larry Goldenberg1, Guy Nir2,3, Septimiu E Salcudean2,3.   

Abstract

Artificial intelligence (AI) - the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data - is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to 'big data' enables the 'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.

Entities:  

Mesh:

Year:  2019        PMID: 31092914     DOI: 10.1038/s41585-019-0193-3

Source DB:  PubMed          Journal:  Nat Rev Urol        ISSN: 1759-4812            Impact factor:   14.432


  57 in total

Review 1.  Online informatics resources to facilitate cancer target and chemical probe discovery.

Authors:  Xuan Yang; Haian Fu; Andrey A Ivanov
Journal:  RSC Med Chem       Date:  2020-04-09

Review 2.  Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.

Authors:  Rodrigo Suarez-Ibarrola; Simon Hein; Gerd Reis; Christian Gratzke; Arkadiusz Miernik
Journal:  World J Urol       Date:  2019-11-05       Impact factor: 4.226

Review 3.  Gut microbiome, big data and machine learning to promote precision medicine for cancer.

Authors:  Giovanni Cammarota; Gianluca Ianiro; Anna Ahern; Carmine Carbone; Andriy Temko; Marcus J Claesson; Antonio Gasbarrini; Giampaolo Tortora
Journal:  Nat Rev Gastroenterol Hepatol       Date:  2020-07-09       Impact factor: 46.802

Review 4.  Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.

Authors:  Yawen Wu; Michael Cheng; Shuo Huang; Zongxiang Pei; Yingli Zuo; Jianxin Liu; Kai Yang; Qi Zhu; Jie Zhang; Honghai Hong; Daoqiang Zhang; Kun Huang; Liang Cheng; Wei Shao
Journal:  Cancers (Basel)       Date:  2022-02-25       Impact factor: 6.639

Review 5.  Looking back and thinking forwards - 15 years of cardiology and cardiovascular research.

Authors:  Jonathan M Kalman; Sergio Lavandero; Felix Mahfoud; Matthias Nahrendorf; Magdi H Yacoub; Dong Zhao
Journal:  Nat Rev Cardiol       Date:  2019-09-30       Impact factor: 32.419

Review 6.  Machine learning for sperm selection.

Authors:  Jae Bem You; Christopher McCallum; Yihe Wang; Jason Riordon; Reza Nosrati; David Sinton
Journal:  Nat Rev Urol       Date:  2021-05-17       Impact factor: 14.432

Review 7.  Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment.

Authors:  Jun Tan; Feng Qin; Jiuhong Yuan
Journal:  Transl Androl Urol       Date:  2021-04

8.  Machine learning for the prediction of severe pneumonia during posttransplant hospitalization in recipients of a deceased-donor kidney transplant.

Authors:  You Luo; Zuofu Tang; Xiao Hu; Shuo Lu; Bin Miao; Songlin Hong; Haiyun Bai; Chen Sun; Jiang Qiu; Huiying Liang; Ning Na
Journal:  Ann Transl Med       Date:  2020-02

9.  Probing metabolic alterations in breast cancer in response to molecular inhibitors with Raman spectroscopy and validated with mass spectrometry.

Authors:  Xiaona Wen; Yu-Chuan Ou; Galina Bogatcheva; Giju Thomas; Anita Mahadevan-Jansen; Bhuminder Singh; Eugene C Lin; Rizia Bardhan
Journal:  Chem Sci       Date:  2020-08-20       Impact factor: 9.969

10.  A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease.

Authors:  Jorge I Vélez; Luiggi A Samper; Mauricio Arcos-Holzinger; Lady G Espinosa; Mario A Isaza-Ruget; Francisco Lopera; Mauricio Arcos-Burgos
Journal:  Diagnostics (Basel)       Date:  2021-05-17
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.