Current applications of artificial intelligence in vascular surgery

Educating Trainees to Treat Peripheral Arterial Disease: Challenges and Opportunities

Educating trainees to treat Peripheral Artery Disease (PAD) carries specific contemporary challenges. The national increase of the prevalence of PAD combined with a significant shortage of vascular surgeons creates a need for concern for future management of this complex disease. Over the past 2 decades, traditional (5 + 2) and integrated (0 + 5) paradigms have fostered trainee annual growth and comparable case distribution and volumes in endovascular and open surgical treatment options have been maintained. Close evaluation into not only the absolute numbers of surgical cases, but the level of trainee involvement in each logged case is recommended. Future implementation of the Entrustable Professional Activity (EPA) modules will hopefully assist in ensuring linear development of surgical skill and judgment. Additionally, advances in individual and systems level techniques to enhance skill acquisition in the form of "off-the job training" and simulation-based training may provide an enhancement to traditional technical training methods. Finally, the possibility and role of artificial intelligence in vascular surgery skill training must not be ignored, but carefully explored and utilized to modernize cognitive and technical skill preparation for trainees in the and delivery of care for PAD patients. Overall, the training residents for the treatment of PAD patients will be associated with new challenges that vascular surgery must embrace and surmount to advance our specialty.

Current Applications and Future Perspectives of Artificial and Biomimetic Intelligence in Vascular Surgery and Peripheral Artery Disease

Artificial Intelligence (AI) made its first appearance in 1956, and since then it has progressively introduced itself in healthcare systems and patients' information and care. AI functions can be grouped under the following headings: Machine Learning (ML), Deep Learning (DL), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Computer Vision (CV). Biomimetic intelligence (BI) applies the principles of systems of nature to create biological algorithms, such as genetic and neural network, to be used in different scenarios. Chronic limb-threatening ischemia (CLTI) represents the last stage of peripheral artery disease (PAD) and has increased over recent years, together with the rise in prevalence of diabetes and population ageing. Nowadays, AI and BI grant the possibility of developing new diagnostic and treatment solutions in the vascular field, given the possibility of accessing clinical, biological, and imaging data. By assessing the vascular anatomy in every patient, as well as the burden of atherosclerosis, and classifying the level and degree of disease, sizing and planning the best endovascular treatment, defining the perioperative complications risk, integrating experiences and resources between different specialties, identifying latent PAD, thus offering evidence-based solutions and guiding surgeons in the choice of the best surgical technique, AI and BI challenge the role of the physician's experience in PAD treatment.

Deciphering Popliteal Artery Aneurysm Patient Diversity: Insights From a Cluster Analysis of the POPART Registry

BACKGROUND

Popliteal artery aneurysms (PAAs) are the most common peripheral aneurysm. However, due to its rarity, the cumulative body of evidence regarding patient patterns, treatment strategies, and perioperative outcomes is limited. This analysis aims to investigate distinct phenotypical patient profiles and associated treatment and outcomes in patients with a PAA by performing an unsupervised clustering analysis of the POPART (Practice of Popliteal Artery Aneurysm Repair and Therapy) registry.

METHODS AND RESULTS

A cluster analysis (using k-means clustering) was performed on data obtained from the multicenter POPART registry (42 centers from Germany and Luxembourg). Sensitivity analyses were conducted to explore validity and stability. Using 2 clusters, patients were primarily separated by the absence or presence of clinical symptoms. Within the cluster of symptomatic patients, the main difference between patients with acute limb ischemia presentation and nonemergency symptomatic patients was PAA diameter. When using 6 clusters, patients were primarily grouped by comorbidities, with patients with acute limb ischemia forming a separate cluster. Despite markedly different risk profiles, perioperative complication rates appeared to be positively associated with the proportion of emergency patients. However, clusters with a higher proportion of patients having any symptoms before treatment experienced a lower rate of perioperative complications.

CONCLUSIONS

The conducted analyses revealed both an insight to the public health reality of PAA care as well as patients with PAA at elevated risk for adverse outcomes. This analysis suggests that the preoperative clinic is a far more crucial adjunct to the patient's preoperative risk assessment than the patient's epidemiological profile by itself.

The state of vascular surgery education in the United States

With the growing proportion of elderly population in the US and a relatively fixed supply of well-trained vascular surgeons, there is a serious concern that we will be facing a shortage of vascular surgery workforce in the near future. One of the main reasons why there is a shortage of vascular surgeons in the US is due to the fact that many students don't get exposed to this field throughout their student lives and a recent survey of medical students from a non-urban tertiary care academic institution showed that early exposure of the medical students to the surgical careers is correlated with an increased interest in the surgical field. This review of the state of vascular surgery education in the US at the undergraduate level describes in detail the importance of an early introduction to vascular surgery in the education curricula, the current state of the education, potential avenues to improve the exposure of students to the field of vascular surgery and the importance of this effort in matching the increasing need for vascular surgeons for an aging population which is likely to require dedicated care by vascular surgeons of the future. At the present time, the two pathways by the Accreditation Council for Graduate Medical Education (ACGME) to obtain dedicated vascular surgery training in the US include either enrolling in a two year clinical fellowship after completion of general surgery training or to match in a five year vascular surgery integrated residency program after successful completion of medical degree.

Objective Methods to Assess Aorto-Iliac Calcifications: A Systematic Review

Vascular calcifications in aorto-iliac arteries are emerging as crucial risk factors for cardiovascular diseases (CVDs) with profound clinical implications. This systematic review, following PRISMA guidelines, investigated methodologies for measuring these calcifications and explored their correlation with CVDs and clinical outcomes. Out of 698 publications, 11 studies met the inclusion criteria. In total, 7 studies utilized manual methods, while 4 studies utilized automated technologies, including artificial intelligence and deep learning for image analyses. Age, systolic blood pressure, serum calcium, and lipoprotein(a) levels were found to be independent risk factors for aortic calcification. Mortality from CVDs was correlated with abdominal aorta calcification. Patients requiring reintervention after endovascular recanalization exhibited a significantly higher volume of calcification in their iliac arteries. Conclusions: This review reveals a diverse landscape of measurement methods for aorto-iliac calcifications; however, they lack a standardized reproducibility assessment. Automatic methods employing artificial intelligence appear to offer broader applicability and are less time-consuming. Assessment of calcium scoring could be routinely employed during preoperative workups for risk stratification and detailed surgical planning. Additionally, its correlation with clinical outcomes could be useful in predicting the risk of reinterventions and amputations.

The Slip and Fall Index: Assessing the risk of slipping and falling on ice

OBJECTIVES

Canadians are at an increased risk of outdoor slip and fall accidents during periods of ice and snow. The aim of this study was to create an index to alert the public of slippery outdoor conditions and promote pedestrian safety.

METHODS

Emergency department (ED) presentations from the four adult hospitals in Calgary, Alberta, Canada, over an 11-year period (January 2008‒December 2018) were extracted and filtered using the ICD-10 code W00 (fall due to ice and snow). Multivariable dispersion-corrected Poisson regression models were used to determine the variables most predictive of these presentations. Month of year, the presence of ice, snow on ground (per 10 cm), and interactions between ice and snow, all up to 3 days prior, were used to create the Slip and Fall Index (SFI).

RESULTS

The dataset included 14,977 slip and fall on ice/snow ED presentations. Females (57.36%, n = 8591) accounted for more presentations than males (42.64%, n = 6386). All months had a significant effect, either being predictive or protective of slip and falls on ice/snow. Current-day ice, snow on ground, and ice up to 3 days prior were predictive of increased presentations. Month and measurements of ice and snow can be input into the SFI, which generates the level of daily risk.

CONCLUSION

The SFI is the first Canadian index with the purpose of measuring the risk of having a slip and fall accident on ice/snow.

Humans use tools: From handcrafted tools to artificial intelligence

Human evolution is instrument based. Humans created tools >2 million years ago to aid them in hunting, gathering, and defense, allowing them to build shelters and farms and transport goods and people over great distances. Written records preserved our knowledge and experiences for future generations. Instruments have greatly influenced surgery. Knives and needles were used by ancient surgeons, whereas lasers, endoscopes, and robotics are used today. Artificial intelligence (AI) is the future of surgical instruments, increasing precision through self-evaluation, but development remains in the early stages. Vascular surgery research and practice has used AI-powered systems that can track patient progress and identify vascular disease risk using deep learning and pattern recognition, as well as improved radiological interpretation of vascular imaging and medicine. Using insights and data-driven recommendations, AI-powered decision support systems could help surgeons in enhancing patient outcomes by providing guidance to navigate complex anatomy and identify anomalies. Robots can assist surgeons in performing risky, complex operations with optimal outcomes. Human expertise and AI will revolutionize surgery, enhancing its safety, precision, and efficacy. Surgical applications of AI raise numerous questions and debates. Data must be representative of all populations, data management must protect the privacy of patients and physicians, and the AI decision-making process must be clarified to produce validated models that can be used ethically. Vascular surgeons' judgment and experience should not be automated. Instead, AI should contribute to the efficiency and effectiveness of vascular surgeons. Human clinicians must interpret AI-generated data, use clinical judgment, and build empathy, compassion, and shared decision-making to sustain doctor-patient relationships. From simple tools to complex modern technologies, the history of tools reveals human creativity. Our environment has been altered by technology, ensuring our survival and growth. AI is still a half-told tale that will inspire and amaze us for years to come.

AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model

Introduction Ensuring access to high-quality information is paramount to facilitating informed surgical decision-making. The use of the internet to access health-related information is increasing, along with the growing prevalence of AI language models such as ChatGPT. We aim to assess the standard of AI-generated patient-facing information through a qualitative analysis of its readability and quality. Materials and methods We performed a retrospective qualitative analysis of information regarding three common vascular procedures: endovascular aortic repair (EVAR), endovenous laser ablation (EVLA), and femoro-popliteal bypass (FPBP). The ChatGPT responses were compared to patient information leaflets provided by the vascular charity, Circulation Foundation UK. Readability was assessed using four readability scores: the Flesch-Kincaid reading ease (FKRE) score, the Flesch-Kincaid grade level (FKGL), the Gunning fog score (GFS), and the simple measure of gobbledygook (SMOG) index. Quality was assessed using the DISCERN tool by two independent assessors. Results The mean FKRE score was 33.3, compared to 59.1 for the information provided by the Circulation Foundation (SD=14.5, p=0.025) indicating poor readability of AI-generated information. The FFKGL indicated that the expected grade of students likely to read and understand ChatGPT responses was consistently higher than compared to information leaflets at 12.7 vs. 9.4 (SD=1.9, p=0.002). Two metrics measure readability in terms of the number of years of education required to understand a piece of writing: the GFS and SMOG. Both scores indicated that AI-generated answers were less accessible. The GFS for ChatGPT-provided information was 16.7 years versus 12.8 years for the leaflets (SD=2.2, p=0.002) and the SMOG index scores were 12.2 and 9.4 years for ChatGPT and the patient information leaflets, respectively (SD=1.7, p=0.001). The DISCERN scores were consistently higher in human-generated patient information leaflets compared to AI-generated information across all procedures; the mean score for the information provided by ChatGPT was 50.3 vs. 56.0 for the Circulation Foundation information leaflets (SD=3.38, p<0.001). Conclusion We concluded that AI-generated information about vascular surgical procedures is currently poor in both the readability of text and the quality of information. Patients should be directed to reputable, human-generated information sources from trusted professional bodies to supplement direct education from the clinician during the pre-procedure consultation process.

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.

Protecting patient safety and privacy in the era of artificial intelligence

The promise of artificial intelligence (AI) in health care has propelled a significant uptrend in the number of clinical trials in AI and global market spending in this novel technology. In vascular surgery, this technology has the ability to diagnose disease, predict disease outcomes, and assist with image-guided surgery. As we enter an era of rapid change, it is critical to evaluate the ethical concerns of AI, particularly as it may impact patient safety and privacy. This is particularly important to discuss in the early stages of AI, as technology frequently outpaces the policies and ethical guidelines regulating it. Issues at the forefront include patient privacy and confidentiality, protection of patient autonomy and informed consent, accuracy and applicability of this technology, and propagation of health care disparities. Vascular surgeons should be equipped to work with AI, as well as discuss its novel risks to patient safety and privacy.

Mature artificial intelligence- and machine learning-enabled medical tools impacting vascular surgical care: A scoping review of late-stage, US Food and Drug Administration-approved or cleared technologies relevant to vascular surgeons

Artificial intelligence and machine learning (AI/ML)-enabled tools are shifting from theoretical or research-only applications to mature, clinically useful tools. The goal of this article was to provide a scoping review of the most mature AI/ML-enabled technologies reviewed and cleared by the US Food and Drug Administration relevant to the field of vascular surgery. Despite decades of slow progress, this landscape is now evolving rapidly, with more than 100 AI/ML-powered tools being approved by the US Food and Drug Administration each year. Within the field of vascular surgery specifically, this review identified 17 companies with mature technologies that have at least one US Food and Drug Administration clearance, all occurring between 2016 and 2022. The maturation of these technologies appears to be accelerating, with improving regulatory clarity and clinical uptake. The early AI/ML-powered devices extend or amplify clinically entrenched platform technologies and tend to be focused on the diagnosis or evaluation of time-sensitive, clinically important pathologies (eg, reading Digital Imaging and Communications in Medicine-compliant computed tomography images to identify pulmonary embolism), or when physician efficiency or time savings is improved (eg, preoperative planning and intraoperative guidance). The majority (>75%) of these technologies are at the intersection of radiology and vascular surgery. It is becoming increasingly important that the contemporary vascular surgeon understands this shifting paradigm, as these once-nascent technologies are finally maturing and will be encountered with increasingly regularity in daily clinical practice.

Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence

Artificial intelligence (AI)-based technologies have garnered interest across a range of disciplines in the past several years, with an even more recent interest in various health care fields, including Vascular Surgery. AI offers a unique ability to analyze health data more quickly and efficiently than could be done by humans alone and can be used for clinical applications such as diagnosis, risk stratification, and follow-up, as well as patient-used applications to improve both patient and provider experiences, mitigate health care disparities, and individualize treatment. As with all novel technologies, AI is not without its risks and carries with it unique ethical considerations that will need to be addressed before its broad integration into health care systems. AI has the potential to revolutionize the way care is provided to patients, including those requiring vascular care.

Considerations for the application of artificial intelligence in vascular surgical education

The rapid adoption of artificial intelligence (AI) into everyday use has presented multiple issues for surgical educators to consider. In this article, the authors discuss some of the ethical aspects of academic integrity and the use of AI. These issues include the importance of understanding the current limits of AI and the inherent biases of the technology. The authors further discuss the ethical considerations of the use of AI in surgical training and in clinical use, with an emphasis on vascular surgery.

Potential applications of artificial intelligence and machine learning on diagnosis, treatment, and outcome prediction to address health care disparities of chronic limb-threatening ischemia

Chronic limb-threatening ischemia (CLTI) is the most advanced form of peripheral artery disease. CLTI has an extremely poor prognosis and is associated with considerable risk of major amputation, cardiac morbidity, mortality, and poor quality of life. Early diagnosis and targeted treatment of CLTI is critical for improving patient's prognosis. However, this objective has proven elusive, time-consuming, and challenging due to existing health care disparities among patients. In this article, we reviewed how artificial intelligence (AI) and machine learning (ML) can be helpful to accurately diagnose, improve outcome prediction, and identify disparities in the treatment of CLTI. We demonstrate the importance of AI/ML approaches for management of these patients and how available data could be used for computer-guided interventions. Although AI/ML applications to mitigate health care disparities in CLTI are in their infancy, we also highlighted specific AI/ML methods that show potential for addressing health care disparities in CLTI.

Applications of artificial intelligence for patients with peripheral artery disease

OBJECTIVE

Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field.

METHODS

We performed a narrative review based on studies reporting applications of AI in patients with PAD. The MEDLINE database was independently searched by two authors using a combination of keywords to identify studies published between January 1995 and December 2021. Three main fields of AI were investigated including natural language processing (NLP), computer vision and machine learning (ML).

RESULTS

NLP and ML brought new tools to improve the screening, the diagnosis and classification of the severity of PAD. ML was also used to develop predictive models to better assess the prognosis of patients and develop real-time prediction models to support clinical decision-making. Studies related to computer vision mainly aimed at creating automatic detection and characterization of arterial lesions based on Doppler ultrasound examination or computed tomography angiography. Such tools could help to improve screening programs, enhance diagnosis, facilitate presurgical planning, and improve clinical workflow.

CONCLUSIONS

AI offers various applications to support and likely improve the management of patients with PAD. Further research efforts are needed to validate such applications and investigate their accuracy and safety in large multinational cohorts before their implementation in daily clinical practice.

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention's generalizability and interoperability with existing systems, as well as the inner settings' data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.