Journal of vascular surgery. Venous and lymphatic disorders · 2026
Adding artificial intelligence to the pulmonary embolism response team improves time to diagnosis and treatment of pulmonary embolism.
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Austin Emily, Reichard Adam, Muck Patrick, Fellner Angela
Аннотация
OBJECTIVE: There is limited data demonstrating the benefit of artificial intelligence (AI) technology for the diagnosis and triage of pulmonary embolism (PE). This study aimed to demonstrate improved time to diagnosis of PE and subsequent anticoagulation and intervention, with the goal of reducing in-hospital mortality. We hypothesized that implementation of AI-assisted computed tomography pulmonary angiogram (CTPA) detection would reduce time to diagnosis, anticoagulation, and intervention compared with the standard radiology-based workflow.
METHODS: A single institution retrospective review from July 2018 to March 2025 was performed to identify patients diagnosed with PE 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), a Food and Drug Administration-cleared, Health Insurance Portability and Accountability Act-compliant AI platform for automated PE detection on CTPA. Time to diagnosis was defined as the interval from CTPA scan completion to AI-generated alert (post-AI cohort) or to the final radiology report issuance (pre-AI cohort). Time to anticoagulation and intervention was measured from the time of confirmed PE diagnosis. In-hospital mortality was also evaluated.
RESULTS: From July 2018 to March 2025, a total of 148 patients were diagnosed with PE 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 [interquartile range (IQR), 53-73] and 65.5 (IQR, 52-73.5) years, respectively. Time to diagnosis improved significantly from 72.5 (IQR, 44.5-93.3) to 40 (IQR, 28-68.2) minutes (P = .00005). Time to anticoagulation was 76 (IQR, 48.3-99.8) vs 61.5 (IQR, 46.8-110) minutes (P = .824, not significant). Time to intervention improved from 1360 (IQR, 1075.5-1790.2) to 1224 (IQR, 601.6-1563) minutes (P = .036). Two in-hospital deaths occurred, both in the pre-AI cohort.
CONCLUSIONS: Implementation of AI-assisted CTPA detection using Viz.ai PE significantly improved time to diagnosis and intervention in patients with acute PE 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.
METHODS: A single institution retrospective review from July 2018 to March 2025 was performed to identify patients diagnosed with PE 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), a Food and Drug Administration-cleared, Health Insurance Portability and Accountability Act-compliant AI platform for automated PE detection on CTPA. Time to diagnosis was defined as the interval from CTPA scan completion to AI-generated alert (post-AI cohort) or to the final radiology report issuance (pre-AI cohort). Time to anticoagulation and intervention was measured from the time of confirmed PE diagnosis. In-hospital mortality was also evaluated.
RESULTS: From July 2018 to March 2025, a total of 148 patients were diagnosed with PE 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 [interquartile range (IQR), 53-73] and 65.5 (IQR, 52-73.5) years, respectively. Time to diagnosis improved significantly from 72.5 (IQR, 44.5-93.3) to 40 (IQR, 28-68.2) minutes (P = .00005). Time to anticoagulation was 76 (IQR, 48.3-99.8) vs 61.5 (IQR, 46.8-110) minutes (P = .824, not significant). Time to intervention improved from 1360 (IQR, 1075.5-1790.2) to 1224 (IQR, 601.6-1563) minutes (P = .036). Two in-hospital deaths occurred, both in the pre-AI cohort.
CONCLUSIONS: Implementation of AI-assisted CTPA detection using Viz.ai PE significantly improved time to diagnosis and intervention in patients with acute PE 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.