Diagnostic Accuracy of AI for Opportunistic Screening of Abdominal Aortic Aneurysm in CT: A Systematic Review and Narrative Synthesis

A bibliometric analysis of abdominal aortic aneurysm (2014-2024)

Background

Abdominal aortic aneurysm (AAA) is a localized bulge of the abdominal aorta, which mainly manifests as a pulsatile mass in the abdomen. Once an abdominal aortic aneurysm ruptures, the patient's life is seriously endangered. Surgery is the preferred treatment for abdominal aortic aneurysm. At present, there has been no comprehensive review of the current status of abdominal aortic aneurysm research. Therefore, this study aimed to identify global trends in abdominal aortic aneurysm research over the last 10 years through bibliometric analysis and to inform clinical practice, research funding allocation, and decision-making.

Methods

We downloaded research articles and reviews on abdominal aortic aneurysm from 1 January 2014, to 1 March 2024, from the Web of Science core collection. CiteSpace (version 6.2.1), RStudio and VOSviewer (version 1.6.18) were used for visual analysis of regional distribution, institutions, authors, keywords and other information.

Results

The number of documents on abdominal aortic aneurysm research increased continuously and has stabilized in recent years. A total of 9,905 publications from 67 countries were published from 1 January 2014, to 1 March 2024. A total of 2,142 (29.52%) studies were from the United States, 1,293 (13.05%) were from China, and 919 (9.28%) were from the United Kingdom. A total of 205 studies were conducted at Stanford University, 172 were conducted at Harvard Medical School, and 165 were conducted at the Mayo Clinic. The top three coauthorship authors were Schermerhorn, Marc L (114); Golledge, Jonathan (102); and De Vries, Jean Paul P.M. (74). The most cocited reference was Chaikof EL, 2018, J Vasc Surg, v67, p. 2; the most cocited journal was the Journal of Vascular Surgery; and the most cocited author was Lederle, FA. "Abdominal aortic aneurysm" was the most frequently used author keyword (2,492). Twenty-five references with strong citation bursts were identified by "CiteSpace". "Artificial intelligence", "clinical outcomes" and "bridging stent" were the primary keywords of emerging research hotspots.

Conclusion

This is the first bibliometric study to comprehensively summarize the research trends in abdominal aortic aneurysm research. This information can help us to identify the current research hotspots and directions. This study will provide extensive help for future research.

The Expansion of Artificial Intelligence in Modifying and Enhancing the Current Management of Abdominal Aortic Aneurysms: A Literature Review

An abdominal aortic aneurysm (AAA) is a pathological dilation that is 3 cm or greater resulting in a bulging or balloon appearance. To meet a personalized therapeutic approach for patients, artificial intelligence (AI) can exhibit an array of applications ranging from decoding patterns from large data sets to predicting new data. The review aims to discuss how AI can assist and improve the standard of care and management plans for these patients. A comprehensive non-systematic literature review was carried out for published material on the use of AI relating to AAAs. The PubMed and Google Scholar databases were used to scout for articles relating to the title of this review. The review included 54 literature papers in this study. AI is involved on a genomic level, which assists in screening, diagnosing, and identifying individual risk factors of a patient. Personalized management plans can be created with AI predictions using patient data to reduce the risk of in-hospital mortality following a repair or due to complications. AI represents a promising group of programs aimed at improving patient management and assisting surgeons in making beneficial decisions to improve the patient's prognosis.

Incidental Cardiovascular Abnormalities in the Abdominal Aortic Aneurysm (AAA) Surveillance Population During the AAA Get Fit Trial: A Case Series and Review of the Literature

Background The prevalence of cardiovascular disease and incidence of major adverse cardiovascular events (MACEs) is very high among the abdominal aortic aneurysm (AAA) surveillance population. Formal assessments of and interventions to reduce cardiovascular risk are not a routine part of the surveillance programme at present. However, its potential importance is highlighted by incidental findings during the AAA Get Fit Trial, a randomised controlled trial which included baseline cardiopulmonary exercise testing (CPET). We speculate that CPET can act as an opportunistic screening programme to identify cardiovascular disease in AAA surveillance patients. Methods The AAA Get Fit Trial was a prospective, randomised controlled trial at a tertiary vascular centre, Manchester University NHS Foundation Trust, conducted between November 2017 and August 2019. Patients underwent CPET at baseline, 8, 16, 24 and 36 weeks as well as clinical history and examination and blood tests. We report on incidental cardiovascular abnormalities diagnosed during the trial. Results Of the 59 participants in the trial, four (6.8%) were identified to have abnormal findings suggestive of unstable cardiovascular disease. On subsequent further investigation, two patients were diagnosed and treated for severe coronary artery disease after abnormal ECG findings were noted during CPET. One patient was diagnosed with unstable angina after obtaining a detailed history on baseline assessment which was treated medically before going on to have a successful elective AAA repair. Conclusions There is a high incidence of MACEs among this high-risk population both pre and perioperatively. Identifying and treating cardiovascular disease among the AAA surveillance population must be a focus of the future AAA screening programme.

Artificial Intelligence-Assisted Sac Diameter Assessment for Complex Endovascular Aortic Repair

PURPOSE

Artificial intelligence (AI) using an automated, deep learning-based method, Augmented Radiology for Vascular Aneurysm (ARVA), has been verified as a viable aide in aneurysm morphology assessment. The aim of this study was to evaluate the accuracy of ARVA when analyzing preoperative and postoperative computed tomography angiography (CTA) in patients managed with fenestrated endovascular repair (FEVAR) for complex aortic aneurysms (cAAs).

MATERIALS AND METHODS

Preoperative and postoperative CTAs from 50 patients (n=100 CTAs) who underwent FEVAR for cAAs were extracted from the picture archiving and communication system (PACS) of a single aortic center equipped with ARVA. All studies underwent automated AI aneurysm morphology assessment by ARVA. Appropriate identification of the outer wall of the aorta was verified by manual review of the AI-generated overlays for each patient. Maximum outer-wall aortic diameters were measured by 2 clinicians using multiplanar reconstruction (MPR) and curved planar reformatting (CPR), and among studies where the aortic wall was appropriately identified by ARVA, they were compared with ARVA automated measurements.

RESULTS

Identification of the outer wall of the aorta was accurate in 89% of CTA studies. Among these, diameter measurements by ARVA were comparable to clinician measurements by MPR or CPR, with a median absolute difference of 2.4 mm on the preoperative CTAs and 1.6 mm on the postoperative CTAs. Of note, no significant difference was detected between clinician measurements using MPR or CPR on preoperative and postoperative scans (range 0.5-0.9 mm).

CONCLUSION

For patients with cAAs managed with FEVAR, ARVA provides accurate preoperative and postoperative assessment of aortic diameter in 89% of studies. This technology may provide an opportunity to automate cAA morphology assessment in most cases where time-intensive, manual clinician measurements are currently required.

CLINICAL IMPACT

In this retrospective analysis of preoperative and postoperative imaging from 50 patients managed with FEVAR, AI provided accurate aortic diameter measurements in 89% of the CTAs reviewed, despite the complexity of the aortic anatomies, and in post-operative CTAs despite metal artifact from stent grafts, markers and embolization materials. Outliers with imprecise automated aortic overlays were easily identified by scrolling through the axial AI-generated segmentation MPR cuts of the entire aorta.This study supports the notion that such emerging AI technologies can improve efficiency of routine clinician workflows while maintaining excellent measurement accuracy when analyzing complex aortic anatomies by CTA.