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The authors of this study aimed to determine the diagnostic performance of a deep learning algorithm for the automated detection of small 18F-FDG-avid pulmonary nodules in positron emission tomography (PET) scans. Their findings suggest that these machine learning algorithms may be able to aid in the detection of small 18F-FDG-avid pulmonary nodules in clinical PET/CT scans, with artificial intelligence (AI) performing significantly better on images with block sequential regularized expectation maximization (BSREM) than ordered subset expectation maximization (OSEM).

Key points

  • The diagnostic value of deep learning for detecting small lung nodules (≤ 2 cm) in PET images using BSREM and OSEM reconstruction was assessed.
  • BSREM yields higher SUV max of small pulmonary nodules as compared to OSEM reconstruction.
  • The use of BSREM translates into a higher detectability of small pulmonary nodules in PET images as assessed with artificial intelligence.

Article: Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance

Authors: Moritz Schwyzer, Katharina Martini, Dominik C. Benz, Irene A. Burger, Daniela A. Ferraro, Ken Kudura, Valerie Treyer, Gustav K. von Schulthess, Philipp A. Kaufmann, Martin W. Huellner & Michael Messerli

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Welcome to the blog on Artificial Intelligence of the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.