• J. Investig. Med. · Oct 2024

    Single-center outcomes of artificial intelligence in management of pulmonary embolism and pulmonary embolism response team activation.

    • Andrew Talon, Chahat Puri, Dylan L Mccreary, Daniel Windschill, Weston Bowker, Yuqing A Gao, Suresh Uppalapu, and Manoj Mathew.
    • Pulmonary/Critical Care, University of Arizona College of Medicine Phoenix, Phoenix, AZ, USA.
    • J. Investig. Med. 2024 Oct 1; 72 (7): 652660652-660.

    AbstractMultidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI) may help improve pulmonary embolism (PE) management with early CT pulmonary angiogram (CTPA) screening and accelerate PERT coordination. This study aimed to test the clinical validity of an FDA-approved PE AI algorithm. CTPA scan data of 200 patients referred due to automated AI detection of suspected PE were retrospectively reviewed. In our institution, all patients suspected of PE received a CTPA. The AI app was then used to analyze CTPA for the presence of PE and calculate the right-ventricle/left-ventricle (RV/LV) ratio. We compared the AI's output with the radiologists' report. Inclusion criteria included segmental PE with and without RV dysfunction and high-risk PE. The primary endpoint was false positive rate. Secondary end points included clinical outcomes according to the therapy selected, including catheter-directed interventions, systemic thrombolytics, and anticoagulation. Fifty-seven of 200 exams (28.5%) were correctly identified as positive for PE by the algorithm. A total of 143 exams (71.5%) were incorrectly reported as positive. In 8% of cases, PERT was consulted. Four patients (7%) received systemic thrombolytics without any complications. There were six patients (10.5%) who developed high-risk PE and underwent thrombectomy, one of whom died. Among 46 patients with acute PE without right heart strain, 44 (95%) survived. The false positive rate of our AI algorithm was 71.5%, higher than what was reported in the AI's prior clinical validity study (91% sensitivity, 100% specificity). A high rate of discordant AI auto-detection of suspected PE raises concerns about its diagnostic accuracy. This can lead to increased workloads for PERT consultants, alarm/notification fatigue, and automation bias. The AI direct notification process to the PERT team did not improve PERT triage efficacy.

      Pubmed     Copy Citation     Plaintext  

      Add institutional full text...

    Notes

     
    Knowledge, pearl, summary or comment to share?
    300 characters remaining
    help        
    You can also include formatting, links, images and footnotes in your notes
    • Simple formatting can be added to notes, such as *italics*, _underline_ or **bold**.
    • Superscript can be denoted by <sup>text</sup> and subscript <sub>text</sub>.
    • Numbered or bulleted lists can be created using either numbered lines 1. 2. 3., hyphens - or asterisks *.
    • Links can be included with: [my link to pubmed](http://pubmed.com)
    • Images can be included with: ![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
    • For footnotes use [^1](This is a footnote.) inline.
    • Or use an inline reference [^1] to refer to a longer footnote elseweher in the document [^1]: This is a long footnote..

    hide…