On the In Vivo Recognition of Kidney Stones Using Machine Learning-Reference-Cited by-同舟云学术

On the In Vivo Recognition of Kidney Stones Using Machine Learning

Published:2024 Issue: Volume:12 Page:10736-10759
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lopez-Tiro Francisco¹^ORCID,Estrade Vincent²^ORCID,Hubert Jacques³^ORCID,Flores-Araiza Daniel¹,Gonzalez-Mendoza Miguel¹,Ochoa Gilberto¹^ORCID,Daul Christian⁴^ORCID

Affiliation:

1. School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico

2. CHU Pellegrin, Bordeaux, France

3. Service d’Urologie de Brabois, CHU Nancy, Vandoeuvre-lès-Nancy, France

4. Centre de Recherche en Automatique de Nancy, UMR 7039, CNRS, Université de Lorraine, Vandoeuvre-lès-Nancy, France

Funder

Azure Sponsorship Credits granted by Microsoft’s Artificial Intelligence (AI) for Good Research Laboratory through the AI for Health Program

French-Mexican Asociación Nacional de Universidades e Instituciones de Educación Superior (ANUIES) Consejo Nacional de de Humanidades, Ciencia y Tecnologia (CONAHCYT) Ecos Nord

Tecnologico de Monterrey through the ‘‘Challenge-Based Research Funding Program 2022’’

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10384337.pdf?arnumber=10384337

Reference72 articles.

1. Kidney stone analysis: “Give me your stone, I will tell you who you are!”

2. Renal stone analysis: why and how?

3. Nephrolithiasis: Treatment, causes, and prevention

4. Chemical and morphological analysis of kidney stones: a double-blind comparative study

5. Stone Composition and Morphology: A Window on Etiology