Machine Learning in Vascular Surgery

Dear Colleagues, 

The integration of machine learning (ML) into vascular surgery is reshaping the field by enhancing diagnostic accuracy, surgical planning, and patient outcomes. As a subset of artificial intelligence, ML uses algorithms to identify patterns in data and make predictions or decisions without being explicitly programmed. In vascular surgery, where precision and timely intervention are critical, ML is proving to be a transformative tool.

One of the most impactful applications of ML in vascular surgery lies in diagnostic imaging. ML algorithms, particularly those using deep learning and convolutional neural networks (CNNs), can analyze vast amounts of imaging data to detect vascular diseases like aneurysms or peripheral artery disease more quickly and accurately. Beyond diagnostics, ML is instrumental in risk stratification and outcome prediction. ML algorithms can analyze patient data—including demographics, lab results, comorbidities and imaging findings—to predict postoperative complications  such as graft failure, wound infection, or stroke. ML is also making strides in procedural guidance and robotic surgery. Algorithms can support intraoperative navigation by overlaying real-time imaging with anatomical maps, enhancing precision in procedures like endovascular aneurysm repair (EVAR) or carotid artery stenting.

Moreover, the role of ML in personalized medicine cannot be overstated. By analyzing patient-specific data, ML can help tailor treatment strategies. For instance, it can help determine the most effective stent type or optimal anticoagulation regimen based on the unique characteristics of each patient. As electronic health records become more standardized and accessible, ML tools can continuously learn from new data, improving over time and adapting to emerging trends in patient care.

As the technology matures and becomes more transparent and reliable, ML is likely to become an indispensable tool for vascular surgeons. The present Special Issue aims to highlight the significant role of ML in vascular surgery. Therefore, we invite authors to propose articles related to the impact of ML in advancing vascular surgery by improving diagnostics, predicting outcomes, guiding procedures, and personalizing care. By embracing ML, the field can move toward safer, more efficient, and more effective patient care.

Dr. George N. Kouvelos
Dr. Konstantinos Spanos
Dr. Konstantinos Dakis
Guest Editors

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• machine learning
• artificial intelligence
• vascular surgery