Adding Artificial Intelligence to the PERT Improves Time to Diagnosis and Treatment of Pulmonary Embolism.

Authors

Emily Austin, Adam Reichard, Patrick Muck, Angela Fellner

Published in

Journal of vascular surgery. Venous and lymphatic disorders. Pages 102598. Aug 08, 2026. Epub Aug 08, 2026.

Abstract

There is limited data demonstrating the benefit of artificial intelligence technology in the diagnosis and triage of pulmonary embolism. Our study aims to demonstrate improved time to diagnosis of pulmonary embolism and subsequently time to anticoagulation and intervention with the goal of reducing in-hospital mortality. We hypothesized that implementation of artificial intelligence-assisted computed tomography pulmonary angiogram detection would reduce time to diagnosis, time to anticoagulation, and time to intervention compared to standard radiology-based workflow.
A single institution retrospective review from July 2018 to March 2025 was performed to identify patients diagnosed with pulmonary embolism who underwent pulmonary angiogram with mechanical thrombectomy and/or thrombolytics. Patients were divided into a pre-AI cohort (July 2018-2022) and a post-AI cohort (2022-March 2025) corresponding to the institutional implementation of Viz.ai PE (Viz.ai, San Francisco, CA), an FDA-cleared, HIPAA-compliant artificial intelligence platform for automated pulmonary embolism detection on computed tomography pulmonary angiogram. Time to diagnosis was defined as the interval from computed tomography pulmonary angiogram scan completion to artificial intelligence-generated alert (post-AI cohort) or to final radiology report issuance (pre-AI cohort). Time to anticoagulation and time to intervention were measured from the time of confirmed pulmonary embolism diagnosis. In-hospital mortality was also evaluated.
From July 2018 to March 2025, 148 patients were diagnosed with pulmonary embolism and underwent endovascular intervention. Twenty-four patients were excluded. Forty-two patients were diagnosed in the pre-AI era and 82 in the post-AI era. The median age was 65 (IQR 53-73) years and 65.5 (IQR 52-73.5) years respectively. Time to diagnosis improved significantly from 72.5 (IQR 44.5-93.3) minutes to 40 (IQR 28-68.2) minutes (p = 0.00005). Time to anticoagulation was 76 (IQR 48.3-99.8) minutes vs 61.5 (IQR 46.8-110) minutes (p = 0.824, not significant). Time to intervention improved from 1360 (IQR 1075.5-1790.2) minutes to 1224 (IQR 601.6-1563) minutes (p = 0.036). Two in-hospital deaths occurred, both in the pre-AI cohort.
Implementation of artificial intelligence-assisted computed tomography pulmonary angiogram detection using Viz.ai PE significantly improved time to diagnosis and time to intervention in patients with acute pulmonary embolism requiring catheter-directed therapy. Time to anticoagulation was not significantly different between groups; this was study was insufficiently powered to detect differences in clinical outcomes including mortality.

PMID:
42570761
Bibliographic data and abstract were imported from PubMed on 09 Aug 2026.

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