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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<p>Heather Cmiel is an accomplished marketing and communications leader with a proven track record of driving business results using integrated marketing, brand and communications strategies combined with execution excellence. She currently serves as the Vice President of Brand and Communications at Aidoc.</p> <p>Cmiel previously led marketing communications for the Medical Solutions Division at 3M Health Care, now Solventum. Her extensive experience also includes key roles at Bellmont Partners, Weber Shandwick and Maccabee Group.</p> <p>She holds a master’s degree in communication from Purdue University, and is an adjunct professor at the University of St. Thomas. Cmiel is an accredited member of the Public Relations Society of America (PRSA), and is a past president of the Minnesota PRSA chapter.</p>

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