Big Data and Artificial Intelligence in Vascular Surgery: Time for Multidisciplinary Cross-Border Collaboration

Access to reliable and meaningful evidence derived from high-quality randomized trials and observational research is essential in clinical decision making and represents evidencebased medicine. Technical achievements over the past decades including the rapid adoption of digital technologies, development of electronic health records (EHRs), widespread high-speed internet access as well as mobile devices, and the rise of artificial intelligence (AI) have opened a new era for clinical research and quality improvement using big data methods. Data used for real-world evidence research are provided from various sources such as EHRs, registries, picture archiving and communication systems (PACS), or even self-monitoring devices. Efforts to generate real-world evidence have rapidly pointed to the importance for collaboration between centers and countries to collect, analyze, and report reliable and representative data to build recommendations for clinical practice. With that aim in mind, international registry collaborations across medical and surgical disciplines have been created. In the field of vascular surgery, VASCUNET is a collaboration of clinical and administrative vascular registries that was created in 1997 at the European Society for Vascular Surgery (ESVS) annual meeting, and which now counts >40 members from 28 different countries. In the United States (US), the Society for Vascular Surgery (SVS) Vascular Quality Initiative (VQI) collected data from >900,000 vascular procedures performed in the US and in Canada. In 2014, the International Consortium of Vascular Registries (ICVR) was founded and is an umbrella for quality improvement launched in collaboration with the SVS-VQI, ESVS-VASCUNET, and the Medical Device Epidemiology Network (MDEpiNet). More recently, the European Vascular Research Collaborative (EVRC) was implemented as a multidisciplinary research collaborative that aims to facilitate European cross-specialty vascular research. In parallel, the creation of a European Health Data Space has become one of the priorities of the European Commission to promote access and exchange of health data among European institutions not only to support healthcare delivery but also for research. The system is expected to be built in accordance with the European Union (EU) general data protection regulation (GDPR) and may bring promising perspectives for international collaboration. While international registries bring great perspectives to enhance knowledge on the management and the outcomes of patients with vascular diseases worldwide, they mainly focus on clinical and administrative data. Imaging takes a central role in the management of patients with vascular diseases and AI has brought new insights in medical imaging by offering new tools that could help to enhance automatic segmentation and analysis to improve detection, classification, or identification of predictive patterns. Machine learning (ML) models often require a large quantity of data to be trained and validated and data representability is crucial to build accurate and efficient models. Building large scale consortiums and platforms to allow to collect, analyze, and share imaging data