What question did this study set out to answer?

To develop and validate a deep learning algorithm for classifying abnormalities in chest radiographs, matching the performance of practicing radiologists.

November 20, 2018Open Access

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

PRPranav RajpurkarHarvard University JIJeremy IrvinStanford University RBRobyn L. BallJackson Laboratory

Key Points

To develop and validate a deep learning algorithm for classifying abnormalities in chest radiographs, matching the performance of practicing radiologists.
Developed a deep learning algorithm based on chest radiographs
Conducted a retrospective comparison with radiologists' diagnoses
Valued performance in classifying clinically important abnormalities
Deep learning algorithm performed at a level comparable to radiologists in diagnosing abnormalities
Potential to improve patient access to chest radiograph diagnostics
Validation indicates robustness for future clinical implementation

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.

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