Performance of AI-Based Automated Classifications of Whole-Body FDG PET in Clinical Practice: The CLARITI Project

Performance of AI-Based Automated Classifications of Whole-Body FDG PET in Clinical Practice: The CLARITI Project

Purpose: To assess the feasibility of a three-dimensional deep convolutional neural network (3D-CNN) for the general triage of whole-body FDG PET in daily clinical practice. Methods: An institutional clinical data warehouse working environment was devoted to this PET imaging purpose. Dedicated reque...

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Bibliographic Details
Main Authors: Berenbaum, A. (Author), Besson, F.L (Author), Bréant, S. (Author), Daniel, C. (Author), Delingette, H. (Author), Durand, E. (Author), Frank, M. (Author), Grimaldi, L. (Author), Hassen-Khodja, C. (Author), Maire, A. (Author), Martel, P. (Author), Poret, C. (Author)
Format: Article
Language:English
Published: MDPI 2023
Subjects:
artificial intelligence
convolutional neural network
deep learning
FDG PET
Online Access:View Fulltext in Publisher
View in Scopus

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