Seminars in Vascular Surgery最新文献

Three-dimensional (3D) printing has been used in medicine with applications in many different fields. 3D printing allows patient education, interventionalists training, preprocedural planning, and assists the interventionalist to improve treatment outcomes. 3D printing represents a potential advancement by allowing the printing of flexible vascular models. In this article, the authors report a clinical case using 3D printing to perform a physician-modified fenestrated endograft. An overview of 3D printing in vascular and endovascular surgery is provided, focusing on its potential applications for training, education, preprocedural planning, and current clinical applications.

Extended reality has brought new opportunities for medical imaging visualization and analysis. It regroups various subfields, including virtual reality, augmented reality, and mixed reality. Various applications have been proposed for surgical practice, as well as education and training. The aim of this review was to summarize current applications of extended reality and augmented reality in vascular surgery, highlighting potential benefits, pitfalls, limitations, and perspectives on improvement.

Current planning of aortic and peripheral endovascular procedures is based largely on manual measurements performed from the 3-dimensional reconstruction of preoperative computed tomography scans. Assessment of device behavior inside patient anatomy is often difficult, and available tools, such as 3-dimensional–printed models, have several limitations. Digital twin (DT) technology has been used successfully in automotive and aerospace industries and applied recently to endovascular aortic aneurysm repair. Artificial intelligence allows the treatment of large amounts of data, and its use in medicine is increasing rapidly. The aim of this review was to present the current status of DTs combined with artificial intelligence for planning endovascular procedures. Patient-specific DTs of the aorta are generated from preoperative computed tomography and integrate aorta mechanical properties using finite element analysis. The same methodology is used to generate 3-dimensional models of aortic stent-grafts and simulate their deployment. Post processing of DT models is then performed to generate multiple parameters related to stent-graft oversizing and apposition. Machine learning algorithms allow parameters to be computed into a synthetic index to predict Type 1A endoleak risk. Other planning and sizing applications include custom-made fenestrated and branched stent-grafts for complex aneurysms. DT technology is also being investigated for planning peripheral endovascular procedures, such as carotid artery stenting. DT provides detailed information on endovascular device behavior. Analysis of DT-derived parameters with machine learning algorithms may improve accuracy in predicting complications, such as Type 1A endoleaks.

Technological advances over the past century have accelerated the pace and breadth of medical and surgical care. From the initial delivery of “telemedicine” over the radio in the 1920s, the delivery of medicine and surgery in the 21^st century is no longer limited by connectivity. The COVID-19 pandemic hastened the uptake of telemedicine to ensure that health care can be maintained despite limited face-to-face contact. Like other areas of medicine, vascular surgery has adopted telemedicine, although its role is not well described in the literature. This narrative review explores how telemedicine has been delivered in vascular surgery. Specific themes of telemedicine are outlined with real-world examples, including consultation, triaging, collaboration, mentoring, monitoring and surveillance, mobile health, and education. This review also explores possible future advances in telemedicine and issues around equity of care. Finally, important ethical considerations and limitations related to the applications of telemedicine are outlined.

e-Health, defined as “the use of new information and communication technologies (ICT) to improve or support health and health care,” has grown in popularity over recent years as a cost-efficient, rapidly adaptable tool to deliver health care education to a wide audience. In the field of vascular disease, for which early detection and risk factor management may greatly influence patient outcomes, application of e-Health educational resources may provide innovative solutions to facilitate evidence-based and patient-centered care provision of care; to enable patients to take a more active role in the management of their long-term vascular health conditions; and to augment their preparation for, and recovery from, surgical procedures.

Natural language processing is a subfield of artificial intelligence that aims to analyze human oral or written language. The development of large language models has brought innovative perspectives in medicine, including the potential use of chatbots and virtual assistants. Nevertheless, the benefits and pitfalls of such technology need to be carefully evaluated before their use in health care. The aim of this narrative review was to provide an overview of potential applications of large language models and artificial intelligence chatbots in the field of vascular surgery, including clinical practice, research, and education. In light of the results, we discuss current limits and future directions.

e-Health technology holds great promise for improving the management of patients with vascular diseases and offers a unique opportunity to mitigate the environmental impact of vascular care, which remains an under-investigated field. The innovative potential of e-Health operates in a complex environment with finite resources. As the expansion of digital health will increase demand for devices, contributing to the environmental burden of electronics and energy use, the sustainability of e-Health technology is of crucial importance, especially in the context of increasing prevalence of cardiovascular diseases. This review discusses the environmental impact of care related to vascular surgery and e-Health innovation, the potential of e-Health technology to mitigate greenhouse gas emissions generated by the health care sector, and to provide leads to research promoting e-Heath technology sustainability. A multifaceted approach, including ethical design, validated eco-audits methodology and reporting standards, technological refinement, electronic and medical devices reuse and recycling, and effective policies is required to provide a sustainable and optimal level of care to vascular patients.

Computational surgery (CS) is an interdisciplinary field that uses mathematical models and algorithms to focus specifically on operative planning, simulation, and outcomes analysis to improve surgical care provision. As the digital revolution transforms the surgical work environment through broader adoption of artificial intelligence and machine learning, close collaboration between surgeons and computational scientists is not only unavoidable, but will become essential. In this review, the authors summarize the main advances, as well as ongoing challenges and prospects, that surround the implementation of CS techniques in vascular surgery, with a particular focus on the care of patients affected by abdominal aortic aneurysms (AAAs). Several key areas of AAA care delivery, including patient-specific modelling, virtual surgery simulation, intraoperative imaging-guided surgery, and predictive analytics, as well as biomechanical analysis and machine learning, will be discussed. The overarching goals of these CS applications is to improve the precision and accuracy of AAA repair procedures, while enhancing safety and long-term outcomes. Accordingly, CS has the potential to significantly enhance patient care across the entire surgical journey, from preoperative planning and intraoperative decision making to postoperative surveillance. Moreover, CS-based approaches offer promising opportunities to augment AAA repair quality by enabling precise preoperative simulations, real-time intraoperative navigation, and robust postoperative monitoring. However, integrating these advanced computer-based technologies into medical research and clinical practice presents new challenges. These include addressing technical limitations, ensuring accuracy and reliability, and managing unique ethical considerations associated with their use. Thorough evaluation of these aspects of advanced computation techniques in AAA management is crucial before widespread integration into health care systems can be achieved.

The COVID-19 pandemic exposed the vulnerabilities of global health care systems, underscoring the need for innovative solutions to meet the demands of an aging population, workforce shortages, and rising physician burnout. In recent years, wearable technology has helped segue various medical specialties into the digital era, yet its adoption in vascular surgery remains limited. This article explores the applications of wearable devices in vascular surgery and explores their potential outlets, such as enhancing primary and secondary prevention, optimizing perioperative care, and supporting surgical training. The integration of artificial intelligence and machine learning with wearable technology further expands its applications, enabling predictive analytics, personalized care, and remote monitoring. Despite the promising prospects, challenges such as regulatory complexities, data security, and interoperability must be addressed. As the digital health movement unfolds, wearable technology could play a pivotal role in reshaping vascular surgery while offering cost-effective, accessible, and patient-centered care.