Deep learning model for diagnosis of venous thrombosis from lower extremity peripheral ultrasound imaging

Po Wei Chen, Bor Yann Tseng, Zhu Han Yang, Chi Hua Yu, Keng Tse Lin, Jhen Nong Chen, Ping Yen Liu

研究成果: Article同行評審

摘要

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.

原文English
文章編號111318
期刊iScience
27
發行號12
DOIs
出版狀態Published - 2024 12月 20

All Science Journal Classification (ASJC) codes

  • 多學科

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