Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation.

Laurens Topff^1,2, Sanne Steltenpool^3,4, Erik R Ranschaert^5,6, Naglis Ramanauskas^7,8, Renee Menezes⁹, Jacob J Visser³, Regina G H Beets-Tan^10,11, Nolan S Hartkamp⁴

¹Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands. l.topff@nki.nl.

²GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands. l.topff@nki.nl.

³Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.

⁴Department of Radiology, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands.

⁵Department of Radiology, St. Nikolaus Hospital, Eupen, Belgium.

⁶Ghent University, Ghent, Belgium.

⁷Oxipit UAB, Vilnius, Lithuania.

⁸Department of Radiology, Nuclear Medicine and Medical Physics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.

⁹Biostatistics Centre, Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.

¹⁰Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.

¹¹GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.

PubMed PMC DOI PDF

Abstract

OBJECTIVES: To evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs. METHODS: A retrospective study was performed in two institutions, a secondary care hospital and tertiary referral oncology centre. Commercially available AI software performed a comparative analysis of chest radiographs a…

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