Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT

Yiting Xie; Benedikt Graf; Parisa Farzam; Brian Baker; Christine Lamoureux; Arkadiusz Sitek

doi:10.1117/12.2607877

4 April 2022 Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT

Yiting Xie, Benedikt Graf, Parisa Farzam, Brian Baker, Christine Lamoureux, Arkadiusz Sitek

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203332 (2022) https://doi.org/10.1117/12.2607877
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

We developed and validated a research-only deep learning (DL) based automatic algorithm to detect thoracic and abdominal aortic aneurysms on contrast and non-contrast CT images and compared its performance with assessments obtained from retrospective radiology reports. The DL algorithm was developed using 556 CT scans. Manual annotations of aorta centerlines and cross-sectional aorta boundaries were created to train the algorithm. Aorta segmentation and aneurysm detection performances were evaluated on 2263 retrospective CT scans (154 thoracic and 176 abdominal aneurysms). Evaluation was performed by comparing the automatically detected aneurysm status to the aneurysm status reported in the radiology reports and the AUC was reported. In addition, a quantitative evaluation was performed to compare the automatically measured aortic diameters to manual diameters on a subset of 59 CT scans. Pearson correlation coefficient was used. For aneurysm detection, the AUC was 0.95 for thoracic aneurysm detection (95% confidence region [0.93, 0.97]) and 0.94 for abdominal aneurysm detection (95% confidence region [0.92, 0.96]). For aortic diameter measurement, the Pearson correlation coefficient was 0.973 (p<0.001).

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Yiting Xie, Benedikt Graf, Parisa Farzam, Brian Baker, Christine Lamoureux, and Arkadiusz Sitek "Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203332 (4 April 2022); https://doi.org/10.1117/12.2607877

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