Artificial intelligence in clinical workflow processes in vascular surgery and beyond

Abstract

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.

Keywords: Abdominal aortic aneurysm; Artificial intelligence; Atherosclerotic cardiovascular disease; Machine learning; Peripheral artery disease.

Figure 1.

Example images of segmentation of the aortic lumen (red) and intraluminal thrombus (green) using PRAEVAorta (Nurea), an automatic segmentation software. Cross-sectional CTA images from a patient with an infrarenal abdominal aortic aneurysm are shown at three segmentation levels (rows): proximal aortic neck, maximum aortic diameter, and aortic bifurcation. For each level of segmentation (columns), the following are shown: original CTA image, fully automatic segmentation (generated by the software), senior and junior surgeon manually corrected segmentation. In the comparison, the results from the fully automated segmentation produced by PRAEVAorta are compared with the senior surgeon’s manual corrected segmentation. Best visualized in the 3D comparison rendering is the lumen common to manual and automatic segmentation (green), the thrombus common to manual and automatic segmentation (yellow), the false negatives (red), and the false positives (blue). Reproduced with permission from Caradu C, Spampinato B, Vrancianu AM, Bérard X, Ducasse E. Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation. J Vasc Surg. 2021;74(1):246–256.e6. CTA, Computed tomography angiography;

Figure 2.

Schematic of four AI-derived clusters of patients with CAD and significant features. Reproduced with permission from Flores AM, Schuler A, Eberhard AV, Olin JW, Cooke JP, Leeper NJ, Shah NH, Ross EG. Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups. J Am Heart Assoc. 2021 Dec 7;10(23):e021976. ABI, ankle‐brachial index; BMI, body mass index; CAD, coronary artery disease; CHF, congestive heart failure; CVA, cerebrovascular accident; LDL, low‐density lipoprotein; MACCE, major adverse cardiovascular and cerebrovascular events; MI, myocardial infarction; and PAD, peripheral artery disease.

Figure 3.

AI-generated synthetic images from standard intravascular ultrasound images utilizing conditional generative adversarial networks (cGANs) can improve cardiovascular imaging and augment the operator’s visual clinical workflow. Reproduced with permission from Olender ML, de la Torre Hernández JM, Athanasiou LS, Nezami FR, Edelman ER. Artificial intelligence to generate medical images: augmenting the cardiologist’s visual clinical workflow. Eur Heart J Digit Health. 2021;2(3):539–544.

Figure 4.

Process framework integration of AI in the healthcare system: problem scoping, intervention design, implementation, and evaluation. Multidisciplinary expertise is needed from (1) user experience, (2) data science, (3) healthcare operations, (4) clinical informatics, (5) evaluation, and (6) ethics assessment. Reproduced with permission from Li RC, Asch SM and Shah NH. Developing a delivery science for artificial intelligence in healthcare. NPJ Digit Med. 2020;3:107.