A Pipeline for Automated Quality Control of Chest Radiographs

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 seven countries.

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Publication Details

Journal
Radiology Artificial Intelligence
Published
2025-03-05
DOI
https://doi.org/10.1148/ryai.240003
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.72
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A Pipeline for Automated Quality Control of Chest Radiographs

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Radiomics and Machine Learning in Medical Imaging
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A Pipeline for Automated Quality Control of Chest Radiographs

Andrew N. Priest, Helmut Prosch, Judith Babar, Markus Holzer, Michael Roberts, Matthew Thorpe, Evis Sala, Carola‐Bibiane Schönlieb, Jan Stanczuk, Xiaodan Xing, Anna Breger, L. Escudero, Jacobus Preller, Effrossyni Gkrania‐Klotsas, Píetro Lió, Jonathan Richard Weir-McCall, E. González-Solares, Georg Langs, Sören Dittmer, Julian Gilbey, N. A. Walton, Ian Selby, Marcel Wassink, Philip Teare, James H.F. Rudd, Emily Jefferson, Anna Korhonen, Martin J. Graves, Tolou Shadbahr, Jing Tang, John A. D. Aston, Nicholas Walton, Ming Li, Nan Yang, Guang Yang, Mishal Patel
article en
2 citations

Abstract

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 seven countries.

Radiology Artificial IntelligenceVol. 7(3)
Università Cattolica del Sacro Cuore (IT), King's College London (GB), University of Cambridge (GB), Royal Papworth Hospital (GB), Cambridge University Hospitals NHS Foundation Trust (GB), Agostino Gemelli University Polyclinic (IT), Cancer Research UK Cambridge Center (GB), Cambridge School (PT), Institute of Astronomy (RU), Medical University of Vienna (AT)
Partnerships for the goals
Openalex Percentile: Top 12%
Radiomics and Machine Learning in Medical Imaging
2.72
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