A novel predictive method for URS and laser lithotripsy using machine learning and explainable AI: results from the FLEXOR international database

database[Title] 2025-05-14

Summary:

CONCLUSION: ML is valuable tool for accurately predicting outcomes by analysing pre-existing datasets. Our model demonstrated strong performance in outcomes and risks prediction, laying the groundwork for development of accessible predictive models.

Link:

https://pubmed.ncbi.nlm.nih.gov/40353928/?utm_source=Other&utm_medium=rss&utm_campaign=pubmed-2&utm_content=12QQbiNmM99eUQGIX1JjHIKcROC1Vzv4sOS-2S_LNI19uG_Yrk&fc=20220129225649&ff=20250514115805&v=2.18.0.post9+e462414

From feeds:

📚BioDBS Bibliography » database[Title]

Tags:

Authors:

Carlotta Nedbal, Vineet Gauhar, Sairam Adithya, Pietro Tramanzoli, Nithesh Naik, Shilpa Gite, Het Sevalia, Daniele Castellani, Frédéric Panthier, Jeremy Y C Teoh, Ben H Chew, Khi Yung Fong, Mohammed Boulmani, Nariman Gadzhiev, Abhishek Gajendra Singh, Thomas R W Herrmann, Olivier Traxer, Bhaskar K Somani

Date tagged:

05/14/2025, 11:58

Date published:

05/12/2025, 06:00