Predicting the Efficacy of Repeated Shockwave Lithotripsy for Treating Patients with Upper Urinary Tract Calculi Using an Artificial Neural Network Model.

Journal: Urology journal

PMID: 38629197

Abstract

PURPOSE: To establish a prediction model for repeated shockwave lithotripsy (SWL) efficacy to help choose an appropriate treatment plan for patients with a single failed lithotripsy, reducing their treatment burden.

Authors

Zhongfan Peng

Hubei University of Medicine of Nursing, Shiyan, China. 20120506@hbmu.edu.cn.
Mingjun Wen

Hubei University of Medicine of Nursing, Shiyan, China. 1305479674@qq.com.
Yunfei Li

Pharmaceutics Department, Institute of Medicinal Biotechnology, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100050, PR China.
Tao He
Jiao Wang

Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Taotao Zhang

Hubei University of Medicine of Nursing, Shiyan, China. zhangtaotao88@sina.com.

Keywords

Adult Aged Female Humans Kidney Calculi Lithotripsy Male Middle Aged Neural Networks, Computer Retreatment Retrospective Studies Treatment Outcome Ureteral Calculi

External Resources

View on PubMed Access via DOI PubMed (38629197)

Predicting the Efficacy of Repeated Shockwave Lithotripsy for Treating Patients with Upper Urinary Tract Calculi Using an Artificial Neural Network Model.

Abstract

Authors

Keywords

External Resources

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