目前的甲状腺结节超声扫描分类系统耗时、劳动密集且主观性强。人工智能（AI）已被证明能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来对甲状腺结节进行分类。我们从2016年1月至2021年6月收集了进行甲状腺结节细针穿刺活检的患者的超声图像。共纳入了139例恶性结节患者和235例良性结节患者作为对照组。图像被输入Swin-T和ResNeSt50模型以对甲状腺结节进行分类。恶性结节患者年龄较小且男性比例较高。Swin-T的平均灵敏度和特异度分别为82.46%和84.29%。ResNeSt50的平均灵敏度和特异度分别为72.51%和77.14%。受试者工作特征分析表明，Swin-T的曲线下面积（AUC=0.91）高于ResNeSt50（AUC=0.82）。麦克马尔检验评估这些模型的性能表明，Swin-T的性能显著优于ResNeSt50。Swin-T分类器可以成为在具有超声图像风险特征的甲状腺结节患者中，帮助医生和患者进行共享决策的有用工具。© Thieme. 保留所有权利。

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.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.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.Thieme. All rights reserved.