Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy

European Radiology



Quinlan D. Buchlak, Cyril H. M. Tang, Jarrel C. Y. Seah, Andrew Johnson, Xavier Holt, Georgina M. Bottrell, Jeffrey B. Wardman, Gihan Samarasinghe, Leonardo Dos Santos Pinheiro, Hongze Xia, Hassan K. Ahmad, Hung Pham, Jason I. Chiang, Nalan Ektas, Michael R. Milne, Christopher H. Y. Chiu, Ben Hachey, Melissa K. Ryan, Benjamin P. Johnston, Nazanin Esmaili, Christine Bennett, Tony Goldschlager, Jonathan Hall, Duc Tan Vo, Lauren Oakden-Rayner, Jean-Christophe Leveque, Farrokh Farrokhi, Richard G. Abramson, Catherine M. Jones, Simon Edelstein & Peter Brotchie


Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists.

Key Points

• This study demonstrated that the use of a comprehensive deep learning system assisted radiologists in the detection of a wide range of abnormalities on non-contrast brain computed tomography scans.

• The deep learning model demonstrated an average area under the receiver operating characteristic curve of 0.93 across 144 findings and significantly improved radiologist interpretation performance.

• The assistance of the comprehensive deep learning model significantly reduced the time required for radiologists to interpret computed tomography scans of the brain.

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