Literature DB >> 34776055

Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists.

Andrew B Chen1, Taseen Haque2, Sidney Roberts2, Sirisha Rambhatla3, Giovanni Cacciamani1, Prokar Dasgupta4, Andrew J Hung5.   

Abstract

The growth and adoption of artificial intelligence has led to impressive results in urology. As artificial intelligence grows more ubiquitous, it is important to establish artificial intelligence literacy in the workforce. To this end, we present a narrative review of the literature of artificial intelligence and machine learning in urology and propose a checklist of reporting standards to improve readability and evaluate the current state of the literature. The listed article demonstrated heterogeneous reporting of methodologies and outcomes, limiting generalizability of research. We hope that this review serves as a foundation for future evaluation of medical research in artificial intelligence.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Machine learning; Review; Urology

Mesh:

Year:  2021        PMID: 34776055      PMCID: PMC9147289          DOI: 10.1016/j.ucl.2021.07.009

Source DB:  PubMed          Journal:  Urol Clin North Am        ISSN: 0094-0143            Impact factor:   2.766


  38 in total

1.  Comparison of Cox regression with other methods for determining prediction models and nomograms.

Authors:  Michael W Kattan
Journal:  J Urol       Date:  2003-12       Impact factor: 7.450

2.  Augmented Bladder Tumor Detection Using Deep Learning.

Authors:  Eugene Shkolyar; Xiao Jia; Timothy C Chang; Dharati Trivedi; Kathleen E Mach; Max Q-H Meng; Lei Xing; Joseph C Liao
Journal:  Eur Urol       Date:  2019-09-17       Impact factor: 20.096

3.  Predicting the Postoperative Outcome of Percutaneous Nephrolithotomy with Machine Learning System: Software Validation and Comparative Analysis with Guy's Stone Score and the CROES Nomogram.

Authors:  Alireza Aminsharifi; Dariush Irani; Sona Tayebi; Taher Jafari Kafash; Tayebeh Shabanian; Hossein Parsaei
Journal:  J Endourol       Date:  2020-02-03       Impact factor: 2.942

4.  The Relative Importance of Race Compared to Health Care and Social Factors in Predicting Prostate Cancer Mortality: A Random Forest Approach.

Authors:  Heidi A Hanson; Christopher Martin; Brock O'Neil; Claire L Leiser; Erik N Mayer; Ken R Smith; William T Lowrance
Journal:  J Urol       Date:  2019-06-27       Impact factor: 7.450

5.  Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection.

Authors:  Bettina Baessler; Tim Nestler; Daniel Pinto Dos Santos; Pia Paffenholz; Vikram Zeuch; David Pfister; David Maintz; Axel Heidenreich
Journal:  Eur Radiol       Date:  2019-12-11       Impact factor: 5.315

6.  Radiomics can predict tumour response in patients treated with Nivolumab for a metastatic renal cell carcinoma: an artificial intelligence concept.

Authors:  Zine-Eddine Khene; Romain Mathieu; Benoit Peyronnet; Romain Kokorian; Anis Gasmi; Fares Khene; Nathalie Rioux-Leclercq; Solène-Florence Kammerer-Jacquet; Shahrokh Shariat; Brigitte Laguerre; Karim Bensalah
Journal:  World J Urol       Date:  2020-07-06       Impact factor: 4.226

7.  Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: a discussion and proposal for an alternative approach.

Authors:  Steve Halligan; Douglas G Altman; Susan Mallett
Journal:  Eur Radiol       Date:  2015-01-20       Impact factor: 5.315

Review 8.  Machine learning and medical education.

Authors:  Vijaya B Kolachalama; Priya S Garg
Journal:  NPJ Digit Med       Date:  2018-09-27

9.  Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients.

Authors:  Zaki Hasnain; Jeremy Mason; Karanvir Gill; Gus Miranda; Inderbir S Gill; Peter Kuhn; Paul K Newton
Journal:  PLoS One       Date:  2019-02-20       Impact factor: 3.240

10.  Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps.

Authors:  Hong Zheng; Jiansong Ji; Liangcai Zhao; Minjiang Chen; An Shi; Linlin Pan; Yiran Huang; Huajie Zhang; Baijun Dong; Hongchang Gao
Journal:  Oncotarget       Date:  2016-09-13
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