An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study

An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study

Abstract Background To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED). Methods In the OPTIMACT trial, 870 patients with suspected nontraumatic...

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Bibliographic Details
Main Authors: Inge A. H. van den Berk, Colin Jacobs, Maadrika M. N. P. Kanglie, Onno M. Mets, Miranda Snoeren, Alexander D. Montauban van Swijndregt, Elisabeth M. Taal, Tjitske S. R. van Engelen, Jan M. Prins, Shandra Bipat, Patrick M. M. Bossuyt, Jaap Stoker, The OPTIMACT study group
Format: Article
Language:English
Published: SpringerOpen 2024-11-01
Series:European Radiology Experimental
Subjects:
Artificial intelligence
Emergency service (hospital)
Multiple pulmonary nodules
Solitary pulmonary nodules
Tomography (x-ray computed)
Online Access:https://doi.org/10.1186/s41747-024-00518-1

Internet

https://doi.org/10.1186/s41747-024-00518-1

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