Dali university, 671003, Dali Yunnan, China.
Department of Ultrasound, Affiliated Hospital of Yunnan University, 650000, Kunming Yunnan, China.
Radiologie (Heidelb). 2023 Nov;63(Suppl 2):64-72. doi: 10.1007/s00117-023-01137-4. Epub 2023 Apr 19.
An artificial intelligence (AI) algorithm based on convolutional neural networks was used in ultrasound diagnosis in order to evaluate its performance in judging the nature of thyroid nodules and nodule classification.
A total of 105 patients with thyroid nodules confirmed by surgery or biopsy were retrospectively analyzed. The properties, characteristics, and classification of thyroid nodules were evaluated by sonographers and by AI to obtain combined diagnoses. Receiver operating characteristic curves were generated to evaluate the performance of AI, the sonographer, and their combined effort in diagnosing the nature of thyroid nodules and classifying their characteristics. In the diagnosis of thyroid nodules with solid components, hypoechoic appearance, indistinct borders, Anteroposterior/transverse diameter ratio > 1(A/T > 1), and calcification performed by sonographers and by AI, the properties exhibited statistically significant differences.
Sonographers had a sensitivity of 80.7%, specificity of 73.7%, accuracy of 79.0%, and area under the curve (AUC) of 0.751 in the diagnosis of benign and malignant thyroid nodules. AI had a sensitivity of 84.5%, specificity of 81.0%, accuracy of 84.7%, and AUC of 0.803. The combined AI and sonographer diagnosis had a sensitivity of 92.1%, specificity of 86.3%, accuracy of 91.7%, and AUC of 0.910.
The efficacy of a combined diagnosis for benign and malignant thyroid nodules is higher than that of an AI-based diagnosis alone or a sonographer-based diagnosis alone. The combined diagnosis can reduce unnecessary fine-needle aspiration biopsy procedures and better evaluate the necessity of surgery in clinical practice.
应用基于卷积神经网络的人工智能（AI）算法进行超声诊断，以评估其在判断甲状腺结节性质和结节分类中的性能。
回顾性分析经手术或活检证实的 105 例甲状腺结节患者。由超声医师和 AI 评估甲状腺结节的性质、特征和分类，得出联合诊断。绘制受试者工作特征曲线，评估 AI、超声医师及其联合诊断在甲状腺结节性质诊断和特征分类中的性能。在评估超声医师和 AI 对甲状腺结节实性成分、低回声、边界不清、前后径/横径比>1（A/T>1）和钙化等特征的诊断效能时，各项参数均具有统计学差异。
超声医师在诊断甲状腺良恶性结节时的灵敏度为 80.7%、特异度为 73.7%、准确度为 79.0%、曲线下面积（AUC）为 0.751；AI 的灵敏度为 84.5%、特异度为 81.0%、准确度为 84.7%、AUC 为 0.803。AI 与超声医师联合诊断的灵敏度为 92.1%、特异度为 86.3%、准确度为 91.7%、AUC 为 0.910。
AI 与超声医师联合诊断甲状腺良恶性结节的效能高于 AI 或超声医师单独诊断，可减少不必要的细针抽吸活检，更好地评估手术的必要性，在临床实践中具有重要意义。
