Artificial Intelligence Improves Initial Evaluation and Follow-up of Patients with AAAs

Timely detection and lifelong monitoring of abdominal aortic aneurysms (AAAs) are necessary to prevent rupture and decrease mortality. Artificial intelligence (AI)-based algorithms can automatically detect AAAs on imaging and radiology reports. This study examines the impact of AI algorithm utilization on AAA detection and care and compares it with historical standard of care. An AI-based AAA detection and measurement algorithm was deployed in the health care system. The software is deployed as a phone application and a desktop analytical tool. The team (vascular surgeons, radiologists, and nurses) gets notifications when an AAA ≥5 cm is detected on any computed tomography (CT) scanner in the network (Fig). It also generates monthly lists of all patients with AAAs for the team to review. A workflow to ensure timely referral and evaluation was established. All CT reports prior to the software deployment were analyzed for the AAA presence using natural language processing of radiology reports. Patients with imaging for known AAA monitoring and AAA screening were excluded. Patients were divided into two groups: "pre-AI" and "post-AI" (prior to and post implementation of AI-driven protocol, respectively). The study compared patient and imaging characteristics, initial evaluation and long-term follow-up, and the timeline between AI-detected scans and AAA repairs. A subgroup analysis to assess the time to evaluation for AAA measuring ≥4 cm was performed. The primary outcome was clinical evaluation within 6 months after imaging detecting AAAs. Patient and imaging characteristics were similar in both groups (Table). A greater proportion of patients underwent AAA evaluation within 6 months after implementation of AI-assisted AAA care (42% vs 18%; P < .001). There was a trend for a shorter evaluation timeline for patients in the post-AI protocol group (22 days vs 83 days; P = .1). Most patients in both groups were seen by vascular surgeons for the initial AAA evaluation and during long-term follow-up. Similar proportions of patients in both groups were treated with statin, aspirin, and antiplatelet medical therapy at the time of initial evaluation. A greater proportion of patients in the post-AI protocol group had long-term follow-up (45% vs 30%; P = .006) and had scheduled appointments for long-term AAA monitoring (99% vs 67%; P < .001). The implementation of the AI-assisted AAA detection and care protocol significantly increased proportion of patients receiving initial AAA evaluation and long-term follow-up care. It also correlated with a decreased timeline to initial evaluation, and for AAAs measuring ≥5 cm, it shortened the time from detection to repair.