Predicting extracorporeal shock wave lithotripsy success with a machine learning nomogram: A pilot study

Using machine learning we have introduced the nomogram predicting single treatment success rate of an ESWL procedure. The application of this ML-nomogram in clinical practice enables the nomogram to continuously improve and refine its outputs as new data become available.

Publication Details

Journal
Editor-in-Chief s Voice List of Authors is an Important Element in a Scientific Publication
Published
2025-12-01
DOI
https://doi.org/10.5173/ceju.2025.0104
Primary Topic
Kidney Stones and Urolithiasis Treatments
Type
article
Field-Weighted Citation Impact
0.00
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article

Predicting extracorporeal shock wave lithotripsy success with a machine learning nomogram: A pilot study

Editor-in-Chief s Voice List of Authors is an Important Element in a Scientific Publication
Kidney Stones and Urolithiasis Treatments
article

Predicting extracorporeal shock wave lithotripsy success with a machine learning nomogram: A pilot study

article en

Abstract

Using machine learning we have introduced the nomogram predicting single treatment success rate of an ESWL procedure. The application of this ML-nomogram in clinical practice enables the nomogram to continuously improve and refine its outputs as new data become available.

Editor-in-Chief s Voice List of Authors is an Important Element in a Scientific PublicationVol. 78(4)
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Openalex Percentile: Top 26%
Kidney Stones and Urolithiasis Treatments
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Predicting extracorporeal shock wave lithotripsy success with a machine learning nomogram: A pilot study · Editor-in-Chief s Voice List of Authors is an Important Element in a Scientific Publication (2025) | TGRS Research Map | TGRS