Use of a Deep Learning Algorithm for Detection and Triage of Cancer-associated Incidental Pulmonary Embolism

Incidental pulmonary embolism (iPE) is a common complication in patients with cancer, and there is often a delay in reporting these studies and a delay between the finalized report and time to treatment. In addition, unreported iPE is common. This retrospective single-center cross-sectional study evaluated the effect of an artificial intelligence (AI) algorithm on the report turnaround time, time to treatment, and detection rate in patients with cancer-associated iPE. Adult patients with cancer were included either before (July 1, 2018, to June 30, 2019) or after (November 1, 2020, to April 30, 2021) implementation of an AI algorithm for iPE detection and triage. The results demonstrated that reported iPE prevalence was significantly higher in the period after AI implementation (2.5% [26 of 1036 studies] vs 0.8% [16 of 1892 studies], P < .001). Both report that the turnaround time (median, 0.66 hour vs 24.68 hours, P < .001) and time to treatment (median, 0.98 hour vs 28.05 hours, P < .001) were significantly shorter after AI implementation. In conclusion, the use of AI for detection and triage of iPE in clinical practice resulted in an increased detection rate of iPE and significantly shorter report turnaround time and time to treatment for patients with cancer-associated iPE. Keywords: Cancer-associated Incidental Pulmonary Embolism, Pulmonary Embolism, Artificial Intelligence, Cancer, CT Imaging © RSNA, 2023.