This paper is in the following e-collection/theme issue:

Urology (36) Machine Learning (1821) Clinical Information and Decision Making (1721) Electronic Health Records (1213) Secondary Use of Clinical Data for Research and Surveillance (433) Decision Support for Health Professionals (1388)

Published on in Vol 11, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33357, first published .
A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study

A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study

A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study

Authors of this article:

Reihaneh Moghisi1 Author Orcid Image ;   Christo El Morr2 Author Orcid Image ;   Kenneth T Pace3, 4 Author Orcid Image ;   Mohammad Hajiha5 Author Orcid Image ;   Jimmy Huang1 Author Orcid Image
Article Authors Cited by (12) Tweetations (2) Metrics

Journals

  1. Sassanarakkit S, Hadpech S, Thongboonkerd V. Theranostic roles of machine learning in clinical management of kidney stone disease. Computational and Structural Biotechnology Journal 2023;21:260 View
  2. Hassan A, Rajesh A, Asaad M, Nelson J, Coert J, Mehrara B, Butler C. A Surgeon’s Guide to Artificial Intelligence-Driven Predictive Models. The American Surgeon 2023;89(1):11 View
  3. Gurevich E, El Hassan B, El Morr C. Equity within AI systems: What can health leaders expect?. Healthcare Management Forum 2023;36(2):119 View
  4. Rashidi E, Langarizadeh M, Sayadi M, Sarkarian M. Machine Learning Models for Predicting the Type and Outcome of Ureteral Stones Treatments. Advanced Biomedical Research 2023;12(1) View
  5. . F, Rasyid N, Atmoko W, Birowo P. Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review. F1000Research 2025;14:16 View
  6. Iparraguirre-Villanueva O, Paucar-Palomino G, Paulino-Moreno C. From data to diagnosis: evaluation of machine learning models in predicting kidney stones. Neural Computing and Applications 2025;37(15):9049 View
  7. . F, Rasyid N, Atmoko W, Birowo P. Artificial Intelligence in the Prediction of Stone-Free Status in Urinary Stone Disease Treated with Extracorporeal Shockwave Lithotripsy: A Systematic Review. F1000Research 2025;14:16 View
  8. Yang R, Zhao D, Ye C, Hu M, Qi X, Li Z. Predicting ESWL success for ureteral stones: a radiomics-based machine learning approach. BMC Medical Imaging 2025;25(1) View
  9. Talyshinskii A, Juliebø-Jones P, Tzelves L, Naik N, Nedbal C, Keulimzhayev N, Panthier F, Pietropaolo A, Somani B. Current state of AI for shockwave lithotripsy: a systematic review from YAU and EAU endourology. World Journal of Urology 2025;43(1) View

Books/Policy Documents

  1. Yamuna V, Stalin Babu G, Vijay Kumar G, Manchala Y. Proceedings of the 6th International Conference on Communications and Cyber Physical Engineering. View
  2. Ayyachamy S, Neha M, Yadalam P. Navigating Innovations and Challenges in Travel Medicine and Digital Health. View

Conference Proceedings

  1. Sisodia A, Jammal M, El Morr C. 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). A Machine Learning Approach to Predict Poor Mental Health of Intimate Partner Violence Survivors View