Deep Learning Approaches for Thrombosis Detection and Risk Assessment Via Ultrasound Imaging: A Scoping Review

Thrombosis, the formation of blood clots within blood vessels, poses serious health risks including pulmonary embolism and post-thrombotic syndrome. Ultrasound (US) imaging is a widely used, non-invasive diagnostic tool owing to its real-time capability and safety profile; however, its effectiveness is often limited by operator dependency and variability in interpretation. This scoping review investigates how deep learning (DL) techniques have been applied to enhance thrombosis detection and risk assessment using US imaging across venous, arterial, and cardiac contexts. A comprehensive literature search was conducted in PubMed and Scopus following PRISMA-ScR methodology, targeting studies that used DL models for thrombus detection, classification, segmentation, or risk prediction in conjunction with vascular US modalities such as B-mode, Doppler, intravascular ultrasound (IVUS), and transesophageal echocardiography (TEE). Out of 233 records initially identified, 22 studies met the eligibility criteria. The most frequently used models included convolutional neural networks (CNNs), U-Net, Residual Neural Networks (ResNet), and Artificial Neural Networks (ANNs). DL models mainly aided deep vein thrombosis (DVT) diagnosis by evaluating vein compressibility and supporting point-of-care ultrasound (POCUS) imaging. Arterial thrombosis applications focused on plaque segmentation and vessel reconstruction using IVUS, while cardiac studies employed TEE to differentiate thrombi from tumours. Studies often reported high sensitivity, specificity, accuracy, and area under the curve (AUC), frequently outperforming traditional rule-based or manual interpretation methods, although considerable variability in datasets and validation approaches was observed. Overall, DL-enhanced US imaging shows great promise for improving diagnostic precision and clinical decision-making in thrombosis care. Future research should prioritize model interpretability, real-world integration, and the development of standardized, publicly accessible datasets.