Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Urinary Tract Calculi VI: Surgical Management01:25

Urinary Tract Calculi VI: Surgical Management

639
Procedures for Kidney StonesMedical intervention is necessary when kidney stones or renal calculi are too large to pass spontaneously (typically greater than 5 millimeters) when stones are accompanied by symptomatic infection (such as fever or pyelonephritis), when they impair kidney function, or when they cause persistent symptoms like severe pain, nausea, or urinary retention. Additionally, patients with only one kidney or those who cannot be treated with medical management also require...
639

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The comparison of synchronous bilateral and unilateral percutaneous nephrolithotomy: Meta-analysis.

Central European journal of urology·2026
Same author

Cytokine-STAT3 Signaling Axis in Clear Cell Renal Cell Carcinoma: Implications for Tumor Microenvironment and Biomarker Discovery.

Cancers·2026
Same author

Accidental coincidences in camera-based high-dimensional entanglement certification.

Optics letters·2025
Same author

Surgical Complications After Kidney Transplantation.

Annals of transplantation·2025
Same author

Global comparison of research ethical review protocols: insights from an international research collaborative.

BJU international·2025
Same author

Measurement of the Vaginal Pressure Profile with the Femfit® and Leakage Events Using a Newly Developed Pad Test during Selected Sports Activities: A Pilot Study.

International urogynecology journal·2025

Related Experiment Video

Updated: Feb 24, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

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

Jan Svihra1, Peter Svihra2,3,4, Igor Sopilko1

  • 1Jessenius Faculty of Medicine, Comenius University, Martin, Slovakia.

Central European Journal of Urology
|February 23, 2026
PubMed
Summary

This study developed a machine learning nomogram to predict extracorporeal shock wave lithotripsy (ESWL) success for kidney stones. The nomogram improves patient selection for ESWL, optimizing treatment outcomes.

Keywords:
ESWLartificial intelligencemachine learningnomogramrenal stone

More Related Videos

Treatment Protocol for Rotator Cuff Calcific Tendinitis Using a Single-Crystal Piezoelectric Focused Shock Wave Source
05:17

Treatment Protocol for Rotator Cuff Calcific Tendinitis Using a Single-Crystal Piezoelectric Focused Shock Wave Source

Published on: December 23, 2022

3.6K

Related Experiment Videos

Last Updated: Feb 24, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K
Treatment Protocol for Rotator Cuff Calcific Tendinitis Using a Single-Crystal Piezoelectric Focused Shock Wave Source
05:17

Treatment Protocol for Rotator Cuff Calcific Tendinitis Using a Single-Crystal Piezoelectric Focused Shock Wave Source

Published on: December 23, 2022

3.6K

Area of Science:

  • Urology
  • Medical Informatics
  • Machine Learning

Background:

  • Kidney stones are a common condition requiring effective treatment.
  • Extracorporeal shock wave lithotripsy (ESWL) is a primary treatment modality for kidney stones.
  • Predicting ESWL success is crucial for optimizing patient selection and treatment planning.

Purpose of the Study:

  • To develop and validate a machine learning (ML)-based clinical nomogram.
  • To predict the success rate of extracorporeal shock wave lithotripsy (ESWL) for kidney stones.
  • To optimize patient selection and treatment outcomes for ESWL.

Main Methods:

  • Retrospective analysis of 102 nephrolithiasis patients' ESWL data (January 2018 - September 2022).
  • Analysis of patient demographics, stone characteristics (size, area, location, density), and treatment parameters (SSD, stent, hydronephrosis).
  • Development of an ML model using Python and scikit-learn, with Linear Discriminant Analysis (LDA) achieving ~70% accuracy.

Main Results:

  • Statistically significant predictors of single-treatment ESWL success included stone size, stone area, and skin-to-stone distance (SSD).
  • The developed nomogram identified high probability of success for SSD ≤8 cm and stone area ≤60 mm², excluding lower pole stones.
  • The LDA model achieved a mean predictive accuracy of approximately 70%.

Conclusions:

  • A novel ML-based nomogram was developed to predict single-treatment ESWL success.
  • This clinical tool can aid in patient selection and improve ESWL treatment efficacy.
  • The nomogram's predictive capability can be continuously refined with accumulating clinical data.