Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 22 min ago
Peer-reviewedLicence not statedBJU International

Kidney stones composition prediction using artificial intelligence ( KiSCAI) study

  • Louise Duffaut
  • Pietro Scilipoti
  • Zine-Eddine Khene
  • Steeve Doizi
  • Vincent Frochot
  • Laurent Berthe
  • Federico Zorzi
  • Nicola Nannola
  • Carlos Gonzalez Gonzalez
  • Jean-Philippe Haymann
  • Emmanuel Letavernier
  • Michel Daudon
3 more authors
  • Olivier Traxer
  • Marie Chicaud
  • Frédéric Panthier
Open at sourcePDFDOICite

Related

  1. Anatomical deformation prediction using artificial intelligence for fusion imaging during aortic endovascular procedures
    Cindy Vannier, Florent Lalys, Jade Hollier-Ben Turkia +2
    Journal of Vascular Surgery Cases and Innovative TechniquesPeer-reviewedCC BY 4.0Full textSystems & synthetic biologyHAL
  2. Artificial Intelligence in Chronic Coronary Syndromes: Linking Cellular Pathways to Precision Risk Prediction
    Ibrahim Antoun, Georgia R. Layton, Riyaz Somani +3
    CellsPeer-reviewedCC BY 4.0Cell biologyCrossref
  3. Artificial Intelligence in the Fight Against Antimicrobial Resistance: Applications in Diagnosis, Susceptibility Prediction, Precision Dosing, and Antibiotic Discovery.
    Susanna Esposito, Valentina Fainardi, Gaia Giorgia Arnesano +2
    Antibiotics (Basel, Switzerland)Peer-reviewedCC BYFull text1 citedMicrobiologyEurope PMC
  4. Kidzoi Enables Cell-Type-Specific Regulatory Activity and Variant Effect Prediction in Kidney
    Arif A. Rather, Dongwon Lee
    bioRxivPreprintAll rights reservedFull textBioinformatics & computational biologybioRxivPMC
  5. Toward Enactive Artificial Intelligence
    Banafsheh Rafiee, R Sutton
    arXiv (Cornell University)PreprintLicence not statedFull text6 citedNeuroscienceOpenAlex

Cite

BibTeXRIS
BibTeX
@article{duffaut2026kidney,
  title = {{Kidney stones composition prediction using artificial intelligence ( KiSCAI) study}},
  author = {Louise Duffaut and Pietro Scilipoti and Zine-Eddine Khene and Steeve Doizi and Vincent Frochot and Laurent Berthe and Federico Zorzi and Nicola Nannola and Carlos Gonzalez Gonzalez and Jean-Philippe Haymann and Emmanuel Letavernier and Michel Daudon and Olivier Traxer and Marie Chicaud and Frédéric Panthier},
  journal = {BJU International},
  year = {2026},
  month = sep,
  doi = {10.1111/bju.70442},
  url = {https://hal.science/hal-05753558v1},
}
RIS
TY  - JOUR
TI  - Kidney stones composition prediction using artificial intelligence ( KiSCAI) study
AU  - Louise Duffaut
AU  - Pietro Scilipoti
AU  - Zine-Eddine Khene
AU  - Steeve Doizi
AU  - Vincent Frochot
AU  - Laurent Berthe
AU  - Federico Zorzi
AU  - Nicola Nannola
AU  - Carlos Gonzalez Gonzalez
AU  - Jean-Philippe Haymann
AU  - Emmanuel Letavernier
AU  - Michel Daudon
AU  - Olivier Traxer
AU  - Marie Chicaud
AU  - Frédéric Panthier
PY  - 2026
DA  - 2026/09/17
JO  - BJU International
DO  - 10.1111/bju.70442
UR  - https://hal.science/hal-05753558v1
SN  - 1464-4096
SN  - 1464-410X
ER  -