Abstract
Deep vein thrombosis (DVT) causes significant healthcare burdens worldwide. This study aims to establish a deep learning model for the diagnosis of DVT from the assessment of vein compressibility. Considering the complexity of ultrasound images, convolutional neural networks with UNet and residual neural network (ResNet) are established for image segmentation, from venous duplex ultrasonographic video images, obtained through standard and portable handheld ultrasound methods. To further evaluate the similarity between the predicted and ground truth images, the structural similarity index (SSIM) is employed. Our deep learning model achieves over 90% accuracy, providing an innovative tool for both images and videos. This study harnesses the power of machine learning to develop an automatic labeling tool that can diagnose DVT by analyzing ultrasonography images. To make the tool more accessible to front-line clinicians, a user-friendly application is created to quickly assess possible clinical severity and enable prompt medical intervention, reducing disease progression.