14486
clinical study

Artificial Intelligence–Based Algorithms Improve Care of Patients With Abdominal Aortic Aneurysm (AAA)

Materials & Methods
This retrospective study evaluated the effect of an AI-based detection and measurement algorithm for abdominal aortic aneurysms (AAA) implemented across a large healthcare system. The tool automatically identified AAAs ≥5 cm from CT imaging and notified the vascular surgery team through mobile and desktop platforms, generating monthly patient lists for review. Data from pre- and post-AI deployment periods were compared using natural language processing to identify previously reported AAAs. The primary endpoint was the rate and timeliness of initial evaluation following incidental AAA detection, with secondary measures assessing long-term follow-up and repair timelines.

Results
Following AI implementation, the proportion of patients receiving initial evaluation increased significantly (42% vs. 18%, p < 0.001). The average time to evaluation decreased from 83 to 22 days (p = 0.1). Long-term follow-up rates improved (45% vs. 30%, p = 0.004), and scheduled monitoring appointments rose markedly (99% vs. 65%, p < 0.001).

Conclusions
Integration of an AI-driven AAA detection and care protocol substantially improved early evaluation, follow-up adherence and care coordination for patients with incidentally detected aneurysms. These findings highlight AI’s potential to enhance surveillance and expedite treatment, ultimately improving outcomes for patients at risk of aneurysm rupture.

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