Literature DB >> 32719928

The prospect of machine learning in predicting post-lithotripsy outcomes.

Linjie Peng1,2,3, Jinyou Pan1,2,3, Ding Yang1,2,3, Wen Zhong4,5,6.   

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Year:  2020        PMID: 32719928     DOI: 10.1007/s00345-020-03377-8

Source DB:  PubMed          Journal:  World J Urol        ISSN: 0724-4983            Impact factor:   4.226


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  1 in total

1.  Comparison of STONE score, Guy's stone score and Clinical Research Office of the Endourological Society (CROES) score as predictive tools for percutaneous nephrolithotomy outcome: a prospective study.

Authors:  Krishnendu Biswas; Shailendra Kumar Gupta; Gopal R Tak; Arvind P Ganpule; Ravindra B Sabnis; Mahesh R Desai
Journal:  BJU Int       Date:  2020-08-04       Impact factor: 5.588

  1 in total
  4 in total

1.  Predicting the Stone-Free Status of Percutaneous Nephrolithotomy With the Machine Learning System: Comparative Analysis With Guy's Stone Score and the S.T.O.N.E Score System.

Authors:  Hong Zhao; Wanling Li; Junsheng Li; Li Li; Hang Wang; Jianming Guo
Journal:  Front Mol Biosci       Date:  2022-05-04

2.  Double-sheath vacuum suction versus vacuum-assisted sheath minimally invasive percutaneous nephrolithotomy for management of large renal stones: single-center experience.

Authors:  Zhong-Hua Wu; Tong-Zu Liu; Xing-Huan Wang; Yong-Zhi Wang; Hang Zheng; Yin-Gao Zhang
Journal:  World J Urol       Date:  2021-05-25       Impact factor: 4.226

3.  Continuous intrapelvic pressure monitoring in flexible ureteroscopy: a bright prospect and some other concerns.

Authors:  Linjie Peng; Wen Zhong
Journal:  World J Urol       Date:  2020-07-08       Impact factor: 4.226

4.  Prospects and Challenges of Artificial Intelligence and Computer Science for the Future of Urology.

Authors:  Rodrigo Suarez-Ibarrola; Arkadiusz Miernik
Journal:  World J Urol       Date:  2020-10       Impact factor: 4.226

  4 in total

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