A deep learning based ultrasound diagnostic tool driven by 3D visualization of thyroid nodules
Yahan Zhou, Chen Chen, Jincao Yao, Jiabin Yu, Bojian Feng, Lin Sui, Yuqi Yan, Xiayi Chen, Yuanzhen Liu, Xiao Zhang, Hui Wang, Qianmeng Pan, Weijie Zou, Qi Zhang, Lin Lü, Chenke Xu, Shengxing Yuan, Qiang He, Xiaofan Ding, Ping Liang, Vicky Y. Wang, Dong Xu
Abstract
Recognizing the limitations of computer-assisted tools for thyroid nodule diagnosis using static ultrasound images, this study developed a diagnostic tool utilizing dynamic ultrasound video, namely Thyroid Nodules Visualization (TNVis), by leveraging a two-stage deep learning framework that involved three-dimensional (3D) visualization. In this multicenter study, 4569 cases were included for framework development, and data from seven hospitals were employed for diagnostic validation. TNVis achieved a Dice similarity coefficient of 0.90 after internal testing. For the external validation, TNVis significantly improved radiologists' performance, reaching an AUC of 0.79, compared to their diagnostic performance without the use of TNVis (AUC: 0.66; p < 0.001) and those with partial assistance (AUC: 0.72; p < 0.001). In conclusion, the TNVis-assisted diagnostic strategy not only significantly improves the diagnostic ability of radiologists but also closely imitates their clinical diagnostic procedures and provides them with an objective 3D representation of the nodules for precise and personalized diagnosis and treatment planning.