Simulation training in vascular and endovascular surgery: A nationwide survey among surgeon educators and trainees in France.

OBJECTIVES: 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 to 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 to 2 hours (34%), 30% expressed satisfaction with access to simulation, while 33% were dissatisfied with communication of simulation training opportunities. CONCLUSION: 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.

Artificial intelligence in vascular surgical decision making.

Despite advances in prevention, detection, and treatment, cardiovascular disease is a leading cause of mortality and represents a major health problem worldwide. Artificial intelligence and machine learning have brought new insights to the management of vascular diseases by allowing analysis of huge and complex datasets and by offering new techniques to develop advanced imaging analysis. Artificial intelligence-based applications have the potential to improve prognostic evaluation and evidence-based decision making and contribute to vascular therapeutic decision making. In this scoping review, we provide an overview on how artificial intelligence could help in vascular surgical clinical decision making, highlighting potential benefits, current limitations, and future challenges.

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.

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.

Automatic Detection of Visceral Arterial Aneurysms on Computed Tomography Angiography Using Artificial Intelligence Based Segmentation of the Vascular System.

Introduction: Visceral arterial aneurysms (VAAs) are life threatening. Due to the paucity of symptoms and rarity of the disease, VAAs are underdiagnosed and underestimated. Artificial intelligence (AI) offers new insights into segmentation of the vascular system, and opportunities to better detect VAAs. This pilot study aimed to develop an AI based method to automatically detect VAAs from computed tomography angiography (CTA). Methods: A hybrid method combining a feature based expert system with a supervised deep learning algorithm (convolutional neural network) was used to enable fully automatic segmentation of the abdominal vascular tree. Centrelines were built and reference diameters of each visceral artery were calculated. An abnormal dilatation (VAAs) was defined as a substantial increase in diameter at the pixel of interest compared with the mean diameter of the reference portion. The automatic software provided 3D rendered images with a flag on the identified VAA areas. The performance of the method was tested in a dataset of 33 CTA scans and compared with the ground truth provided by two human experts. Results: Forty-three VAAs were identified by human experts (32 in the coeliac trunk branches, eight in the superior mesenteric artery, one in the left renal, and two in the right renal arteries). The automatic system accurately detected 40 of the 43 VAAs, with a sensitivity of 0.93 and a positive predictive value of 0.51. The mean number of flag areas per CTA was 3.5 ± 1.5 and they could be reviewed and checked by a human expert in less than 30 seconds per CTA. Conclusion: Although the specificity needs to be improved, this study demonstrates the potential of an AI based automatic method to develop new tools to improve screening and detection of VAAs by automatically attracting clinicians' attention to suspicious dilatations of the visceral arteries.