HARNESSING THE POWER OF ARTIFICIAL INTELLIGENCE TO REVOLUTIONIZE VASCULAR SURGERY: A COMPREHENSIVE REVIEW OF CURRENT APPLICATIONS, PROSPECTS, AND POTENTIAL CHALLENGES

: Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various aspects of healthcare, including vascular surgery. This narrative review aims to provide a comprehensive overview of AI's current applications, prospects, and potential challenges in vascular surgery. The article follows a structured approach, beginning with an introduction to the fundamentals of AI and its relevance to vascular surgery. The methodology section outlines the literature search strategy and selection criteria for identifying relevant studies. The results section delves into the key findings, categorized into subtopics such as predictive modeling, image analysis, surgical planning, and intraoperative guidance. The discussion section critically analyzes the implications of AI in vascular surgery, addressing its potential benefits, limitations, and ethical considerations. Finally, the conclusion summarizes the main points and provides recommendations for future research and clinical implementation. Throughout the article, each assertion is supported by reliable references sourced from reputable databases. This review aims to advance AI and vascular surgery knowledge by presenting up-to-date information, robust evidence, and valuable insights.


INTRODUCTION
Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various aspects of healthcare, including vascular surgery.AI encompasses a range of techniques, such as machine learning, deep learning, and natural language processing that enable computers to learn from data and perform tasks that typically require human intelligence (1).AI's 1 Centro Universitário do Pará -CESUPA. 2 Universidade Tiradentes. 3 UniRV -FAMERV (Campus Rio Verde). 4Santa Casa de Misericórdia de Vitoria Higher School of Sciences -EMESCAM. 5Molecular Dynamics and Modeling Laboratory (DynMolLab): application in vascular surgery is a technological advancement and a potential game-changer for patient outcomes.The promise it holds for improving patient care, optimizing surgical planning, and enhancing healthcare efficiency is immense (2).
In recent years, a significant surge of interest has been seen among vascular surgeons in leveraging AI to address their complex challenges.From predictive modeling for risk stratification to image analysis for diagnosis and surgical planning, AI has piqued the interest of these professionals by demonstrating its potential to augment their capabilities and improve patient care (3).While integrating AI into clinical practice holds great promise, it raises important questions.The reliability, interpretability, and ethical implications of AI in healthcare are crucial considerations that must be addressed as we move forward (4).This narrative review aims to provide a comprehensive overview of AI's current applications, prospects, and potential challenges in vascular surgery.By synthesizing the latest research findings and insights from experts in the field, this article seeks to inform and guide vascular surgeons, researchers, and policymakers in navigating the rapidly evolving landscape of AI in healthcare.

METHODOLOGY
To identify relevant studies for this narrative review, a comprehensive literature search was conducted using several reputable databases, including Scopus, Web of Science, PubMed, ScienceDirect, and IEEE Xplore.The search strategy employed a combination of keywords related to artificial intelligence, machine learning, vascular surgery, and specific applications such as predictive modeling, image analysis, and surgical planning.
The search was limited to articles published in English between 2015 and 2023 to ensure the most recent and relevant research was included.Both original research articles and review papers were considered.The reference lists of the included articles were also manually screened to identify additional relevant studies.
The selection criteria for the articles included in this review were based on their relevance to the topic, methodological quality, and potential impact on vascular surgery.Preference was given to studies with robust designs, large sample sizes, and clear reporting of methods and results.Review articles that provided comprehensive overviews or meta-analyses of specific applications of AI in vascular surgery were also included.
The selected articles were carefully examined, and the key findings were extracted and synthesized to provide a coherent narrative.The results section was organized into subtopics based on the specific applications of AI in vascular surgery, allowing for a structured presentation of the evidence.

Predictive Modeling and Risk Stratification
AI has shown great promise in developing predictive models for risk stratification in vascular surgery.Machine learning algorithms, such as logistic regression, support vector machines, and neural networks, have been applied to analyze large datasets and identify patterns that can predict surgical outcomes and complications.This advancement in predictive modeling instills optimism for the future of vascular surgery, promising improved patient outcomes and more efficient healthcare delivery (2).By leveraging the power of AI, surgeons can make more informed decisions and provide personalized care to their patients.

Image Analysis and Diagnosis
AI has also demonstrated remarkable capabilities in analyzing medical images, particularly in the context of vascular surgery.Deep learning algorithms, such as CNNs, have been applied to various imaging modalities, including ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI), to detect and characterize vascular pathologies automatically.This reassures the audience about the accuracy of diagnoses in vascular surgery, enhancing patient care and reducing the workload associated with manual image interpretation (6).
Abdolmanafi et al. developed a deep learning model for the automatic segmentation and quantification of abdominal aortic aneurysms (AAAs) on CT scans (7).The model achieved high accuracy in delineating the aneurysm boundaries and measuring the maximum diameter, with a mean absolute error of 1.8 mm compared to manual measurements.This automated approach can streamline the diagnostic process, reduce inter-observer variability, and facilitate monitoring aneurysm growth over time.
Another promising application of AI in image analysis is the detection of peripheral artery disease (PAD).Negoro et al. employed a CNN to analyze color Doppler ultrasound images of the lower extremities and identify signs of PAD, such as arterial stenosis and occlusion (8).The model achieved a sensitivity of 91% and a specificity of 94% in detecting PAD, demonstrating its potential as a screening tool in primary care settings.
These AI-powered image analysis techniques can assist vascular surgeons in making accurate diagnoses, reducing the workload associated with manual image interpretation and improving the efficiency of the diagnostic process.By providing objective and reproducible measurements, AI can enhance the reliability and consistency of vascular imaging assessments.

Surgical Planning and Simulation
AI has the potential to revolutionize surgical planning and simulation in vascular surgery.By integrating patient-specific anatomical data, hemodynamic simulations, and predictive modeling, AI can provide surgeons with valuable insights to optimize surgical strategies and improve patient outcomes (9,10).
One example is using AI to predict the risk of endoleak after endovascular aneurysm repair  (12,13).The model incorporated patient-specific geometries, blood flow patterns, and plaque characteristics to generate personalized risk scores.This approach can assist surgeons in selecting the most appropriate stent design and optimizing the procedural technique to minimize the risk of restenosis.Furthermore, AI-powered virtual reality (VR) and augmented reality (AR) platforms have emerged as promising surgical training and preoperative planning tools.These immersive technologies allow surgeons to interact with patient-specific 3D models, simulate surgical procedures, and rehearse complex endovascular interventions in a risk-free environment (14,15).AI algorithms can analyze the surgeon's performance, provide real-time feedback, and suggest optimal surgical approaches based on the patient's anatomy and pathology.
Leveraging AI for surgical planning and simulation can enhance vascular surgeons' decisionmaking, improve procedural efficiency, and potentially reduce complications.These AI-powered tools can also facilitate novice surgeons' training and skill acquisition, ultimately improving the quality of vascular surgical care.
Intraoperative Guidance and Robotic Assistance AI can potentially transform intraoperative guidance and robotic assistance in vascular surgery.
By integrating real-time imaging data, surgical navigation systems, and robotic platforms, AI can enhance the precision and safety of vascular interventions (16).
One promising application is using AI for real-time vessel segmentation and tracking during endovascular procedures.Iezzi et al. developed a deep learning algorithm that can automatically segment and track the aorta and its branches in fluoroscopic images (17).The algorithm achieved a dice similarity coefficient 0.92, indicating its high accuracy in delineating vascular anatomy.This real-time guidance can assist surgeons in navigating complex vascular structures, reducing radiation exposure and minimizing the risk of complications.AI can also be integrated into robotic surgical platforms to enhance the dexterity and precision of vascular interventions.Maier-Hein et al. developed an AI-powered robotic system for endovascular navigation and catheter manipulation (18).The system utilized deep reinforcement learning algorithms to optimize the robotic control and adapt to patient-specific anatomical variations.In a preclinical study, the AI-powered robotic system demonstrated superior efficiency and safety compared to manual catheter manipulation, highlighting its potential to improve the outcomes of endovascular procedures.
Furthermore, AI can be applied to analyze intraoperative data streams, such as hemodynamic monitoring and electrophysiological signals, to provide real-time decision support during vascular surgery.Tan et al. developed a machine learning model that analyzed intraoperative hemodynamic parameters to predict the risk of postoperative complications in patients undergoing carotid endarterectomy (19).The model achieved an AUC of 0.87, demonstrating its potential to guide intraoperative management and optimize patient care.
Vascular surgeons can enhance surgical procedures' precision, safety, and efficiency by leveraging AI for intraoperative guidance and robotic assistance.These AI-powered tools can reduce human errors, minimize invasiveness, and potentially improve patient outcomes, paving the way for a new era of intelligent and personalized vascular surgery.

DISCUSSION
Integrating AI in vascular surgery holds immense potential to revolutionize the field and improve patient care.The results presented in this review highlight the diverse applications of AI, ranging from predictive modeling and risk stratification to image analysis, surgical planning, and intraoperative guidance.These AI-powered tools have demonstrated remarkable accuracy, efficiency, and potential to augment the capabilities of vascular surgeons.
One of AI's key benefits in vascular surgery is its ability to analyze vast amounts of data and identify patterns that may be difficult for humans to discern.This can lead to more accurate risk predictions, personalized treatment strategies, and early detection of complications (2).AI algorithms can also automate time-consuming tasks, such as image segmentation and measurements, freeing surgeons' time to focus on higher-level decision-making and patient care (6).
Moreover, AI-powered surgical planning and simulation tools can give surgeons unprecedented insights into patient-specific anatomy and hemodynamics (9,10).By leveraging virtual reality and augmented reality technologies, surgeons can rehearse complex procedures, optimize surgical approaches, and potentially reduce the learning curve associated with new techniques (14,15).This can improve surgical outcomes, reduce complications, and enhance patient safety.
However, integrating AI in vascular surgery also raises essential considerations and challenges.One primary concern is the interpretability and transparency of AI algorithms (4).Many AI models, profound learning algorithms, operate as "black boxes," making it difficult to understand how they arrive at their predictions or decisions.This lack of transparency can hinder the trust and acceptance of AI among surgeons and patients alike.Efforts are underway to develop explainable AI models that provide clear insights into their decision-making process, but this remains an active area of research (20).
Another challenge is the potential for bias and fairness issues in AI algorithms.If the training data used to develop AI models is biased or unrepresentative of the target population, the resulting predictions and recommendations may perpetuate or even amplify existing disparities in healthcare (21).It's crucial to ensure that AI models are developed using diverse and representative datasets and rigorously tested for fairness and generalizability.
The integration of AI in vascular surgery also raises important ethical considerations.As AI algorithms become more autonomous and influential in clinical decision-making, questions arise regarding the allocation of responsibility and liability in case of adverse events (4).Establishing clear guidelines and frameworks for the ethical development, deployment, and governance of AI in healthcare is essential.This will ensure that the technology is used in a way that prioritizes patient safety, autonomy, and well-being.
Despite these challenges, the potential benefits of AI in vascular surgery are too significant to ignore.As the technology continues to evolve and mature, it is crucial for vascular surgeons to actively engage in developing, validating, and implementing AI tools.Collaboration between clinicians, researchers, and industry partners will be essential to ensure that AI is developed and deployed in a responsible, evidence-based manner (16).

CONCLUSION
This narrative review has provided a comprehensive overview of AI's current applications, prospects, and potential challenges in vascular surgery.The results demonstrate the remarkable potential of AI to transform various aspects of vascular surgical care, from predictive modeling and risk stratification to image analysis, surgical planning, and intraoperative guidance.Vascular surgeons can augment their capabilities, improve patient outcomes, and advance the field by leveraging the power of machine learning, deep learning, and other AI techniques.
However, integrating AI in vascular surgery raises essential considerations and challenges, including interpretability, bias, fairness, and ethics.It is crucial for the vascular surgery community to actively engage in the responsible development, validation, and implementation of AI tools, ensuring that the technology is used in a way that prioritizes patient safety, autonomy, and well-being.
Future research should focus on developing explainable AI models, addressing bias and fairness issues, and establishing clear guidelines and frameworks for the ethical deployment of AI in healthcare.
Collaboration between clinicians, researchers, and industry partners will be essential to realizing AI's full potential in vascular surgery while mitigating its potential risks and challenges.
As AI continues to evolve and mature, it has the potential to revolutionize the field of vascular surgery, enabling more personalized, precise, and patient-centered care.By embracing this transformative technology and actively shaping its development and implementation, vascular One notable example is the use of AI to predict the risk of postoperative complications in patients undergoing vascular surgery.Madani et al. developed a machine learning model that utilized preoperative clinical data, including patient demographics, comorbidities, and laboratory values, to predict the likelihood of postoperative complications (3).The model demonstrated high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.84, indicating its potential to assist in risk stratification and informed decision-making.Similarly, Li et al. applied deep learning techniques to predict readmission and mortality risk in patients undergoing carotid endarterectomy (5).The authors used a convolutional neural network (CNN) to analyze preoperative computed tomography angiography (CTA) images and extract relevant features.The CNN model outperformed traditional risk prediction models, highlighting the potential of AI to improve risk assessment in vascular surgery.These predictive models can aid vascular surgeons in identifying high-risk patients, optimizing preoperative planning, and implementing targeted interventions to mitigate potential complications.

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EVAR).Charalambous et al. developed a machine learning model that combined preoperative CT imaging features and patient characteristics to predict the occurrence of type II endoleaks (11).The model achieved an AUC of 0.81, demonstrating its potential to guide patient selection and inform postoperative surveillance strategies.AI can also be applied to simulate the hemodynamic behavior of vascular systems and predict the outcomes of surgical interventions.Fillingham et al. and Wang et al. used computational fluid dynamics (CFD) simulations, coupled with machine learning algorithms, to predict the risk of restenosis after carotid artery stenting