Automatic measurement of maximal diameter of abdominal aortic aneurysm on computed tomography angiography using artificial intelligence

Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

The aim of our study is to validate a totally automated deep learning (DL)-based segmentation pipeline to screen abdominal aortic aneurysms (AAA) in computed tomography angiography (CTA) scans. We retrospectively evaluated 73 thoraco-abdominal CTAs (48 AAA and 25 control CTA) by means of a DL-based segmentation pipeline built on a 2.5D convolutional neural network (CNN) architecture to segment lumen and thrombus of the aorta. The maximum aortic diameter of the abdominal tract was compared using a threshold value (30 mm). Blinded manual measurements from a radiologist were done in order to create a true comparison. The screening pipeline was tested on 48 patients with aneurysm and 25 without aneurysm. The average diameter manually measured was 51.1 ± 14.4 mm for patients with aneurysms and 21.7 ± 3.6 mm for patients without aneurysms. The pipeline correctly classified 47 AAA out of 48 and 24 control patients out of 25 with 97% accuracy, 98% sensitivity, and 96% specificity. The automated pipeline of aneurysm measurements in the abdominal tract reported a median error with regard to the maximum abdominal diameter measurement of 1.3 mm. Our approach allowed for the maximum diameter of 51.2 ± 14.3 mm in patients with aneurysm and 22.0 ± 4.0 mm in patients without an aneurysm. The DL-based screening for AAA is a feasible and accurate method, calling for further validation using a larger pool of diagnostic images towards its clinical use.

Artificial intelligence in clinical workflow processes in vascular surgery and beyond

In the past decade, artificial intelligence (AI)-based applications have exploded in health care. In cardiovascular disease, and vascular surgery specifically, AI tools such as machine learning, natural language processing, and deep neural networks have been applied to automatically detect underdiagnosed diseases, such as peripheral artery disease, abdominal aortic aneurysms, and atherosclerotic cardiovascular disease. In addition to disease detection and risk stratification, AI has been used to identify guideline-concordant statin therapy use and reasons for nonuse, which has important implications for population-based cardiovascular disease health. Although many studies highlight the potential applications of AI, few address true clinical workflow implementation of available AI-based tools. Specific examples, such as determination of optimal statin treatment based on individual patient risk factors and enhancement of intraoperative fluoroscopy and ultrasound imaging, demonstrate the potential promise of AI integration into clinical workflow. Many challenges to AI implementation in health care remain, including data interoperability, model bias and generalizability, prospective evaluation, privacy and security, and regulation. Multidisciplinary and multi-institutional collaboration, as well as adopting a framework for integration, will be critical for the successful implementation of AI tools into clinical practice.