Poster award in San Diego
We are happy to announce that paper “Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT” authored by Arkadiusz Sitek with colleagues from IBM and vRad Virtual Radiologic (Yiting Xie, Benedikt Graf, Parisa Farzam, Brian Baker, Christine Lamoureux) received poster award cum laude at SPIE Medical Imaging Conference in San Diego.
The Medical Imaging community is about sharing important research and the latest advancements to help move research and technology into the future. This meeting supports leading researchers doing important work that is why being awarded is so huge honor and success.
The paper described 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).
Link to the publication: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12033/1203332/Multi-inst[…]-deep-learning-model-for-fully/10.1117/12.2607877.short
Congratulations!
