12169
clinical study

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

Accuracy, Incidental Pulmonary Embolism, Turnaround Time

Materials & Methods

This study assessed the impact of a deep learning AI algorithm (Aidoc) on the detection and management of incidental pulmonary embolism (iPE) in cancer patients. Data from two time periods were compared: before (July 1, 2018 to June 30, 2019) and after (Nov. 1, 2020 to April 30, 2021) the implementation of the AI algorithm.

Results

There was a significant increase in iPE detection after the implementation of the AI algorithm. The prevalence of iPE rose from 0.8% to 2.5% (p<.001). Additionally, there was a dramatic reduction in median report turnaround time and time to treatment. The median report turnaround time decreased from 24.68 hours to 0.66 hours (p<.001), and the time to treatment was reduced from 28.05 hours to 0.98 hours (p<.001).

Conclusions

The AI algorithm significantly improved both the triage of iPE and the efficiency of reporting and treatment for cancer patients.

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