Diagnostic accuracy of a commercially available deep learning algorithm in supine chest radiographs following trauma

BJR. First published online 18 Mar 2022.

Authors

Jacob Gipson, Victor Tang, Jarrel Seah, Helen Kavnoudias, Adil Zia, Robin Lee, Biswadev Mitra and Warren Clements

Abstract
Objectives

Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validated. This study aimed to evaluate the performance of a commercially available deep convolutional neural network – Annalise CXR V1.2 (Annalise.ai)- for detection of traumatic injuries on supine chest radiographs.

Methods

Chest radiographs with a CT performed within 24 h in the setting of trauma were retrospectively identified at a level one adult trauma centre between January 2009 and June 2019. Annalise.ai assessment of the chest radiograph was compared to the radiologist report of the chest radiograph. Contemporaneous CT report was taken as the ground truth. Agreement with CT was measured using Cohen’s κ and sensitivity/specificity for both AI and radiologists were calculated.

Results

There were 1404 cases identified with a median age of 52 (IQR 33–69) years, 949 male. AI demonstrated superior performance compared to radiologists in identifying pneumothorax (p = 0.007) and segmental collapse (p = 0.012) on chest radiograph. Radiologists performed better than AI for clavicle fracture (p = 0.002), humerus fracture (p < 0.0015) and scapula fracture (p = 0.014). No statistical difference was found for identification of rib fractures and pneumomediastinum.

Conclusion

The evaluated AI performed comparably to radiologists in interpreting chest radiographs. Further evaluation of this AI program has the potential to enable it to be safely incorporated in clinical processes.

This is an open access article distributed in accordance with the Creative Commons Attribution (CC BY 4.0) Unported license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by/4.0/

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