CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm.

Journal: Urolithiasis

PMID: 39499273

Abstract

OBJECTIVES: To develop a deep learning (DL) model based on computed tomography (CT) images to predict the success of extracorporeal shock wave lithotripsy (SWL) treatment for patients with ureteral stones larger than 1 cm.

Authors

Rijin Song

Department of Urology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, Jiangsu Province, China.
Bo Liu

Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China.
Huixin Xu

Department of Urology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, Jiangsu Province, China. xuhuixin1123@126.com.

Keywords

Adult Deep Learning Female Humans Lithotripsy Male Middle Aged Predictive Value of Tests Retrospective Studies Tomography, X-Ray Computed Treatment Outcome Ureter Ureteral Calculi

External Resources

View on PubMed Access via DOI PubMed (39499273)

CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm.

Abstract

Authors

Keywords

External Resources

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