Artificial Intelligence–Based Algorithms Improve Care of Patients with AAA

Abstract

Background Timely detection and monitoring of abdominal aortic aneurysms (AAAs) are necessary to prevent ruptures and decrease mortality. Artificial intelligence (AI)-based algorithms can automatically detect the presence of AAA on imaging and radiology reports. The goal of this study is to examine the impact of AI utilization on AAA detection and care while comparing it to historical standard of care. Methods AI-based AAA detection and measurement algorithm was deployed in the healthcare system. The software can be used as a phone application and a desktop analytical tool. The team (vascular surgeons, radiologists, and nurses) gets notifications when AAA ≥5 cm is detected on any computed tomography imaging in the network. It also generates monthly lists of all patients with AAA for the team to review. A workflow to ensure timely referral and evaluation was established. All computed tomography 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 2 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 initial evaluation after incidental detection of AAA. Results Patient and imaging characteristics were similar in both groups. A greater proportion of patients underwent initial AAA evaluation after implementation of AI-assisted AAA care (42% vs. 18%, P < 0.001). There was a trend for a shorter evaluation timeline for patients in the post-AI protocol group (22 days vs. 83 days, P = 0.1). 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 = 0.004) and had scheduled appointments for long-term AAA monitoring (99% vs. 65%, P < 0.001). Conclusion 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 AAA measuring ≥5 cm, it shortened the time from detection to repair.