DOI: 10.1148/ryai.240003 ISSN: 2638-6100
A Pipeline for Automated Quality Control of Chest Radiographs
Ian A. Selby, Eduardo González Solares, Anna Breger, Michael Roberts, Lorena Escudero Sánchez, Judith Babar, James H. F. Rudd, Nicholas A. Walton, Evis Sala, Carola-Bibiane Schönlieb, Jonathan R. Weir-McCall, , Michael Roberts, Sören Dittmer, Ian Selby, Anna Breger, Matthew Thorpe, Julian Gilbey, Jonathan R. Weir-McCall, Judith Babar, Effrossyni Gkrania-Klotsas, Jacobus Preller, Lorena Escudero Sánchez, Andrew Priest, Anna Korhonen, Emily Jefferson, Georg Langs, Helmut Prosch, Martin Graves, Guang Yang, Xiaodan Xing, Yang Nan, Ming Li, Jan Stanczuk, Jing Tang, Tolou Shadbahr, Philip Teare, Mishal Patel, Marcel Wassink, Markus Holzer, Eduardo González Solares, Nicholas Walton, Pietro Lió, James H. F. Rudd, John A.D. Aston, Evis Sala, Carola-Bibiane Schönlieb“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. This article presents a suite of quality control tools for chest radiographs based on traditional and artificial intelligence methods, developed and tested with data from 39 centers in 7 countries. Published under a CC BY 4.0 license.