First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels.

Jungblut, Lisa; Blüthgen, Christian; Polacin, Malgorzata; Messerli, Michael; Schmidt, Bernhard; Euler, Andre; Alkadhi, Hatem; Frauenfelder, Thomas; Martini, Katharina

Jungblut, Lisa; Blüthgen, Christian; Polacin, Malgorzata; Messerli, Michael; Schmidt, Bernhard; Euler, Andre; Alkadhi, Hatem; Frauenfelder, Thomas; Martini, Katharina.

Afiliação

Jungblut L; From the Institute of Diagnostic and Interventional Radiology.
Blüthgen C; From the Institute of Diagnostic and Interventional Radiology.
Polacin M; From the Institute of Diagnostic and Interventional Radiology.
Messerli M; Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Schmidt B; Siemens Healthineers, Forchheim, Germany.
Euler A; From the Institute of Diagnostic and Interventional Radiology.
Alkadhi H; From the Institute of Diagnostic and Interventional Radiology.
Frauenfelder T; From the Institute of Diagnostic and Interventional Radiology.
Martini K; From the Institute of Diagnostic and Interventional Radiology.

Invest Radiol ; 57(2): 108-114, 2022 02 01.

Article em En | MEDLINE | ID: mdl-34324462

Assuntos

Inteligência Artificial; Fótons; Computadores; Imagens de Fantasmas; Tomografia Computadorizada por Raios X/métodos

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Fótons Tipo de estudo: Diagnostic_studies Idioma: En Revista: Invest Radiol Ano de publicação: 2022 Tipo de documento: Article País de publicação: Estados Unidos

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