Étude et rapport

Comparison of artificial intelligence and human- based prediction and stratification of the risk of long-term kidney allograft failure

GRATUIT

Auteur(s) :

Gillian Divard, Marc Raynaud, Vasishta S. Tatapudi, Basmah Abdalla, Elodie Bailly, Maureen Assayag, Yannick Binois, Raphael Cohen, Huanxi Zhang, Camillo Ulloa, Kamila Linhares, Helio S. Tedesco, Christophe Legendre, Xavier Jouven, Robert A. Montgomery, Carmen Lefaucheur, Olivier Aubert & Alexandre Loupy

Éditeur(s) :

NATURE

Date de publication :23/11/2022

9 pages

EN BREF ...

"Clinical decisions are mainly driven by the ability of physicians to apply risk stratification to patients. However, this task is difficult as it requires complex integration of numerous parameters and is impacted by patient heterogeneity. We sought to evaluate the ability of transplant physicians to predict the risk of long-term allograft failure and compare them to a validated artificial intelligence (AI) prediction algorithm. We randomly selected 400 kidney transplant recipients from a qualified dataset of 4000 patients. For each patient, 44 features routinely collected during the first-year post- transplant were compiled in an electronic health record (EHR). We enrolled 9 transplant physicians at various career stages. At 1-year post-transplant, they blindly predicted the long- term graft survival with probabilities for each patient. Their predictions were compared with those of a validated prediction system (iBox)." En bref issu de l'étude.

Rédacteur(s) de la fiche : Beesens TEAM


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Conclusio

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