Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

Pranav Rajpurkar; Jeremy Irvin; Robyn L. Ball; Kaylie Zhu; Brandon Yang; Hershel Mehta; Tony Duan; Daisy Yi Ding; Aarti Bagul; Curtis P. Langlotz; Bhavik N. Patel; Kristen W. Yeom; Katie Shpanskaya; Francis G. Blankenberg; Jayne Seekins; Timothy J. Amrhein; David A. Mong; Safwan S. Halabi; Evan J. Zucker; Andrew Y. Ng; Matthew P. Lungren

doi:10.1371/journal.pmed.1002686

Abstract

In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics.

Keywords

MedicineChest radiographRadiologyReceiver operating characteristicConvolutional neural networkRadiographyArtificial intelligencePleural effusionDeep learningMedical diagnosisMedical physicsMachine learningComputer science

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Publication Info

Year: 2018
Type: article
Volume: 15
Issue: 11
Pages: e1002686-e1002686
Citations: 1263
Access: Closed

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APA Style

                            
                                
                                    Pranav Rajpurkar, 
                                
                                    Jeremy Irvin, 
                                
                                    Robyn L. Ball
                                
                                et al.
                            
                            (2018). 
                            Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. 
                            PLoS Medicine
                            , 15
                            (11)
                            , e1002686-e1002686.
                            https://doi.org/10.1371/journal.pmed.1002686
                        

Identifiers

DOI: 10.1371/journal.pmed.1002686