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: Arnaud Berenbaum, Hervé Delingette, Aurélien Maire, Cécile Poret, Claire Hassen-Khodja, Stéphane Bréant, Christel Daniel, Patricia Martel, Lamiae Grimaldi, Marie Frank, Emmanuel Durand, Florent L. Besson
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Applied Sciences
Subjects:
FDG PET
artificial intelligence
deep learning
convolutional neural network
Online Access:https://www.mdpi.com/2076-3417/13/9/5281

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https://www.mdpi.com/2076-3417/13/9/5281

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