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.

引言当前的甲状腺结节超声扫描分类系统耗时、费力且主观。人工智能（AI）已被证明可以提高预测甲状腺结节恶性率的准确性。本研究旨在展示最先进的 Swin Transformer 对甲状腺结节进行分类。

材料与方法前瞻性收集2016年1月至2021年6月接受甲状腺结节细针抽吸活检患者的超声图像。纳入139例甲状腺恶性结节患者，235例良性结节患者作为对照。将图像输入 Swin-T 和 ResNeSt50 模型以对甲状腺结节进行分类。

结果与良性结节患者相比，恶性结节患者更年轻，男性可能性更大。Swin-T的平均敏感性和特异性分别为82.46%和84.29%。ResNeSt50的平均敏感性和特异性分别为72.51%和77.14%。受试者操作特征分析显示，Swin-T 的曲线下面积（AUC=0.91）高于 ResNeSt50（AUC=0.82）。评估这些模型性能的 McNemar 测试表明，Swin-T 的性能明显优于 ResNeSt50。

Swin-T 分类器可以成为帮助医生和甲状腺结节患者共同决策的有用工具，特别是对于那些具有超声模式高风险特征的患者。