https://scholars.lib.ntu.edu.tw/handle/123456789/636154
標題: | Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System | 作者: | Fu, Chia Po Yu, Ming Jen Huang, Yao Sian CHIOU-SHANN FUH RUEY-FENG CHANG |
關鍵字: | deep learning | ResNeSt50 | sonography | Swin-T | thyroid nodule | 公開日期: | 1-一月-2023 | 卷: | 131 | 期: | 10 | 來源出版物: | Experimental and Clinical Endocrinology and Diabetes | 摘要: | Introduction The current ultrasound scan classification system for thyroid nodules is time-consuming, labor-intensive, and subjective. Artificial intelligence (AI) has been shown to increase the accuracy of predicting the malignancy rate of thyroid nodules. This study aims to demonstrate the state-of-the-art Swin Transformer to classify thyroid nodules. Materials and Methods Ultrasound images were collected prospectively from patients who received fine needle aspiration biopsy for thyroid nodules from January 2016 to June 2021. One hundred thirty-nine patients with malignant thyroid nodules were enrolled, while 235 patients with benign nodules served as controls. Images were fed to Swin-T and ResNeSt50 models to classify the thyroid nodules. Results Patients with malignant nodules were younger and more likely male compared to those with benign nodules. The average sensitivity and specificity of Swin-T were 82.46% and 84.29%, respectively. The average sensitivity and specificity of ResNeSt50 were 72.51% and 77.14%, respectively. Receiver operating characteristics analysis revealed that the area under the curve of Swin-T was higher (AUC=0.91) than that of ResNeSt50 (AUC=0.82). The McNemar test evaluating the performance of these models showed that Swin-T had significantly better performance than ResNeSt50. Swin-T classifier can be a useful tool in helping shared decision-making between physicians and patients with thyroid nodules, particularly in those with high-risk characteristics of sonographic patterns. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/636154 | ISSN: | 09477349 | DOI: | 10.1055/a-2122-5585 |
顯示於: | 資訊工程學系 |
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