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EJNMMI Physics|February 3, 2022
Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancerPablo Borrelli, José Luis Loaiza Góngora, Reza Kaboteh, et al.
Journal of Nuclear Medicine Technology|November 15, 2008
Can nuclear medicine technologists assess whether a myocardial perfusion rest study is required?Lena Johansson, Milan Lomsky, Peter Gjertsson, et al.
Nuclear Medicine Communications|January 2, 2007
Automated interpretation of PET/CT images in patients with lung cancerHenrik Gutte, David Jakobsson, Fredrik Olofsson, et al.
Clinical Physiology and Functional Imaging|April 28, 2006
Decision support for the initial triage of patients with acute coronary syndromesSven-Erik Olsson, Mattias Ohlsson, Hans Ohlin, et al.
European Journal of Nuclear Medicine and Molecular Imaging|April 27, 2022
Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physiciansElin Trägårdh, Olof Enqvist, Johannes Ulén, et al.
Clinical Physiology and Functional Imaging|August 31, 2006
The added value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy and artificial neural networksPeter Gjertsson, Milan Lomsky, Jens Richter, et al.
Clinical Physiology and Functional Imaging|April 28, 2006
Patient gender and radiopharmaceutical tracer is of minor importance for the interpretation of myocardial perfusion images using an artificial neural networkKristina Tägil, S Richard Underwood, Glyn Davies, et al.
BJU International|April 25, 2015
Bone Scan Index predicts outcome in patients with metastatic hormone-sensitive prostate cancerMads H Poulsen, Janne Rasmussen, Lars Edenbrandt, et al.
PET Clinics|November 23, 2021
Applications of Artificial Intelligence in <sup>18</sup>F-Sodium Fluoride Positron Emission Tomography/Computed Tomography:: Current State and Future DirectionsSriram S Paravastu, Navid Hasani, Faraz Farhadi, et al.
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine|February 3, 2012
An artificial neural network to safely reduce the number of ambulance ECGs transmitted for physician assessment in a system with prehospital detection of ST elevation myocardial infarctionJakob L Forberg, Ardavan Khoshnood, Michael Green, et al.
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