Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.

Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is crucial to define the extent of the disease and, when suitable, the consequent resective or ablative procedure, in order to guarantee a radical treatment without sacrificing an excessive volume of healthy liver. Once mainly performed manually, with notable cost in terms of time and human energies, vessel segmentation is currently realized through the application of artificial intelligence (AI), which has gained increased interest and development of the field. Many different AI-driven models adopted for this aim have been described and can be grouped into different categories: thresholding methods, edge- and region-based methods, model-based methods, and machine learning models. The latter includes neural network and deep learning models that now represent the principal algorithms exploited for vessel segmentation. The present narrative review describes how liver vessel segmentation can be realized through AI models, with a summary of model results in terms of accuracy, and an overview on the future progress of this topic.

Large language models and artificial intelligence chatbots in vascular surgery.

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.

3-Dimensional printing in vascular disease: From manufacturer to clinical use.

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 and augmented reality in vascular surgery: Opportunities and challenges.

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.

Computational surgery in the management of patients with abdominal aortic aneurysms: Opportunities, challenges, and future directions.

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.

e-Health and environmental sustainability in vascular surgery.

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.

Simulation Training in Vascular and Endovascular Surgery: A Nationwide Survey Among Surgeon Educators and Trainees in France.

BACKGROUND: Vascular surgical training is evolving towards simulation-based methods to enhance skill development, ensure patient safety, and adapt to changing regulations. This study aims to investigate the utilization of simulation training among vascular surgeons in France, amidst ongoing shifts in teaching approaches and educational reforms. METHODS: A national survey assessed the experiences and perceptions of vascular surgery professionals regarding simulation training. Participation was open to self-reported health professionals specialized (or specializing) in vascular surgery, including interns or fellows. Participants were recruited through various channels, and data were collected via a questionnaire covering participant characteristics, simulation experiences, and perceptions. RESULTS: Seventy-six participants, predominantly male (74%) took part in the survey. While 58% reported access to simulation laboratories, only 17% had organized simulation sessions 1-3 times a year, and 5% had sessions more than 10 times annually. High fidelity simulators were available in 57% of institutions, while low fidelity simulators were available in 50%. Regarding funding, 20% received financial assistance for training, predominantly from industry (18%). One-third of the participants experienced 9 or more sessions (34%), lasting between 1 and 2 hours (34%), 30% expressed satisfaction with access to simulation, while 33% were dissatisfied with communication of simulation training opportunities. CONCLUSIONS: Despite recognizing the benefits of simulation training, its integration into vascular surgery education in France remains incomplete. Challenges such as limited access and communication barriers hinder widespread adoption. Collaborative efforts are needed to ensure uniformity and enhance the effectiveness of simulation training in vascular surgery education.

Use of Artificial Intelligence With Deep Learning Approaches for the Follow-up of Infrarenal Endovascular Aortic Repair.

INTRODUCTION: Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adverse events. METHODS: Patients who underwent EVAR at a tertiary hospital from January 2017 to March 2020 with at least 2 follow-up computed tomography angiography (CTA) were analyzed using PRAEVAorta 2 (Nurea). The software was compared to the ground truth provided by human experts using Sensitivity (Se), Specificity (Sp), Negative Predictive Value (NPV), and Positive Predictive Value (PPV). Endovascular aortic repair-related adverse events were defined as aneurysm-related death, rupture, endoleak, limb occlusion, and EVAR-related re-interventions. RESULTS: Fifty-six patients were included with a median imaging follow-up of 27 months (interquartile range [IQR]: 20-40). There were no significant differences overtime in the evolution of maximum aneurysm diameters (55.62 mm [IQR: 52.33-59.25] vs 54.34 mm [IQR: 46.13-59.47]; p=0.2162) or volumes (130.4 cm3 [IQR: 113.8-171.7] vs 125.4 cm3 [IQR: 96.3-169.1]; p=0.1131) despite a -13.47% decrease in the volume of thrombus (p=0.0216). PRAEVAorta achieved a Se of 89.47% (95% confidence interval [CI]: 80.58 to 94.57), a Sp of 91.25% (95% CI: 83.02 to 95.70), a PPV of 90.67% (95% CI: 81.97 to 95.41), and an NPV of 90.12% (95% CI: 81.70 to 94.91) in detecting endoleaks. Endovascular aortic repair-related adverse events were associated with global volume modifications with an area under the curve (AUC) of 0.7806 vs 0.7277 for maximum diameter. The same trend was observed for endoleaks (AUC of 0.7086 vs 0.6711). CONCLUSIONS: The AI-based software PRAEVAorta enabled a detailed anatomic characterization of aortic remodeling post-EVAR and showed its potential interest for automatic detection of endoleaks during follow-up. The association of aortic aneurysmal volume with EVAR-related adverse events and endoleaks was more robust compared with maximum diameter. CLINICAL IMPACT: The integration of PRAEVAorta AI software into clinical practice promises a transformative shift in post-EVAR surveillance. By offering precise and rapid detection of endoleaks and comprehensive anatomic assessments, clinicians can expect enhanced diagnostic accuracy and streamlined patient management. This innovation reduces reliance on manual measurements, potentially reducing interpretation errors and shortening evaluation times. Ultimately, PRAEVAorta's capabilities hold the potential to optimize patient care, leading to more timely interventions and improved outcomes in endovascular aortic repair.

Serum S100B Level in the Management of Pediatric Minor Head Trauma: A Randomized Clinical Trial.

Importance: Minor head trauma (HT) is one of the most common causes of hospitalization in children. A diagnostic test could prevent unnecessary hospitalizations and cranial computed tomographic (CCT) scans. Objective: To evaluate the effectiveness of serum S100B values in reducing exposure to CCT scans and in-hospital observation in children with minor HT. Design, Setting, and Participants: This multicenter, unblinded, prospective, interventional randomized clinical trial used a stepped-wedge cluster design to compare S100B biomonitoring and control groups at 11 centers in France. Participants included children and adolescents 16 years or younger (hereinafter referred to as children) admitted to the emergency department with minor HT. The enrollment period was November 1, 2016, to October 31, 2021, with a follow-up period of 1 month for each patient. Data were analyzed from March 7 to May 29, 2023, based on the modified intention-to-treat and per protocol populations. Interventions: Children in the control group had CCT scans or were hospitalized according to current recommendations. In the S100B biomonitoring group, blood sampling took place within 3 hours after minor HT, and management depended on serum S100B protein levels. If the S100B level was within the reference range according to age, the children were discharged from the emergency department. Otherwise, children were treated as in the control group. Main Outcomes and Measures: Proportion of CCT scans performed (absence or presence of CCT scan for each patient) in the 48 hours following minor HT. Results: A total of 2078 children were included: 926 in the control group and 1152 in the S100B biomonitoring group (1235 [59.4%] boys; median age, 3.2 [IQR, 1.0-8.5] years). Cranial CT scans were performed in 299 children (32.3%) in the control group and 112 (9.7%) in the S100B biomonitoring group. This difference of 23% (95% CI, 19%-26%) was not statistically significant (P = .44) due to an intraclass correlation coefficient of 0.32. A statistically significant 50% reduction in hospitalizations (95% CI, 47%-53%) was observed in the S100B biomonitoring group (479 [41.6%] vs 849 [91.7%]; P < .001). Conclusions and Relevance: In this randomized clinical trial of effectiveness of the serum S100B level in the management of pediatric minor HT, S100B biomonitoring yielded a reduction in the number of CCT scans and in-hospital observation when measured in accordance with the conditions defined by a clinical decision algorithm. Trial Registration: ClinicalTrials.gov Identifier: NCT02819778.

Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery.

Objective: The use of Natural Language Processing (NLP) has attracted increased interest in healthcare with various potential applications including identification and extraction of health information, development of chatbots and virtual assistants. The aim of this comprehensive literature review was to provide an overview of NLP applications in vascular surgery, identify current limitations, and discuss future perspectives in the field. Data sources: The MEDLINE database was searched on April 2023. Review methods: The database was searched using a combination of keywords to identify studies reporting the use of NLP and chatbots in three main vascular diseases. Keywords used included Natural Language Processing, chatbot, chatGPT, aortic disease, carotid, peripheral artery disease, vascular, and vascular surgery. Results: Given the heterogeneity of study design, techniques, and aims, a comprehensive literature review was performed to provide an overview of NLP applications in vascular surgery. By enabling identification and extraction of information on patients with vascular diseases, such technology could help to analyse data from healthcare information systems to provide feedback on current practice and help in optimising patient care. In addition, chatbots and NLP driven techniques have the potential to be used as virtual assistants for both health professionals and patients. Conclusion: While Artificial Intelligence and NLP technology could be used to enhance care for patients with vascular diseases, many challenges remain including the need to define guidelines and clear consensus on how to evaluate and validate these innovations before their implementation into clinical practice.

Machine Learning and Omics Analysis in Aortic Aneurysm.

Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field.

Natural Language Processing for Literature Search in Vascular Surgery: A Pilot Study Testing an Artificial Intelligence Based Application.

Introduction: The use of natural language processing (NLP) for a literature search has been poorly investigated in vascular surgery so far. The aim of this pilot study was to test the applicability of an artificial intelligence (AI) based mobile application for literature searching in a topic related to vascular surgery. Technique: A focused scientific question was defined to evaluate the performance of the AI application for a literature search and compare the results with the ground truth provided via a traditional literature search performed by human experts. Using pre-defined keywords, the literature search was performed automatically by the AI application through different steps, including quality assessment based on evaluation of the information available and quality filters using indicators of level of evidence, selection of publications based on relevancy filters using NLP, summarisation, and visualisation of the publications via the mobile app. A traditional literature search performed by human experts required 10 hours to check 154 original articles, among which 26 (16.9%) were truly related to the question, 63 (40.9%) related to the field but not to the specific question, and 65 (42.2%) were unrelated. The AI based search was performed in less than one hour, and, compared with traditional search, the method identified 17 original articles (48.6%) truly related to the question (p < .010), 18 (51.4%) related to the field but not to the specific question (p = .26), and no unrelated publications (p < .001). Fifteen truly related articles (88.2%) were identified jointly by the two methods. No significant difference was observed regarding the median number of citations, year of publications, and impact factor of journals. Discussion: The AI based method enabled a targeted, focused, and time saving literature search, although the selection of publications was not completely exhaustive. These results suggest that such an AI driven application is a complementary tool to help researchers and clinicians for continuous education and dissemination of knowledge.

Artificial intelligence-based predictive models in vascular diseases.

Cardiovascular disease represents a source of major health problems worldwide, and although medical and technical advances have been achieved, they are still associated with high morbidity and mortality rates. Personalized medicine would benefit from novel tools to better predict individual prognosis and outcomes after intervention. Artificial intelligence (AI) has brought new insights to cardiovascular medicine, especially with the use of machine learning techniques that allow the identification of hidden patterns and complex associations in health data without any a priori assumptions. This review provides an overview on the use of artificial intelligence-based prediction models in vascular diseases, specifically focusing on aortic aneurysm, lower extremity arterial disease, and carotid stenosis. Potential benefits include the development of precision medicine in patients with vascular diseases. In addition, the main challenges that remain to be overcome to integrate artificial intelligence-based predictive models in clinical practice are discussed.