【摘要】 目的 利用深度学习算法构建甲状腺功能异常患者乳腺结节智能超声诊断系统。方法 回顾性收集2023年1月至2024年2月期间新疆医科大学第一附属医院超声库中的178例甲状腺功能异常患者的乳腺超声图像（共计969张）并将其作为训练集，利用深度学习算法构建甲状腺功能异常患者乳腺结节智能超声诊断系统。另回顾性收集2024年3月至2025年1月期间新疆医科大学第一附属医院超声库中的81例甲状腺功能异常患者的乳腺超声图像（共计445张）并将其作为验证集，并利用上述系统对甲状腺功能异常患者是否患乳腺结节进行诊断，分析影像学医师、智能超声诊断系统对甲状腺功能异常患者乳腺结节的诊断效能，并通过Kappa检验探索超声科医师、智能超声诊断系统与“金标准”的一致性。结果 训练集与验证集患者的年龄、甲状腺功能异常类型、病程、乳腺结节数量等临床资料比较差异均无统计学意义（P>0.05）。训练集中，智能超声诊断系统诊断单张乳腺超声图像的耗时[（0.04±0.01）min]低于超声科医师[（12.36±2.58）min],t=63.709、P<0.001。验证集中，甲状腺功能异常患者经智能超声诊断系统检出乳腺结节的灵敏度、特异度、准确率、曲线下面积（area under the curve,AUC）分别为97.87%(46/47)、100%(34/34)、98.77%(80/81)、0.997[95%CI(0.951,1.000)]；经超声科医师检出乳腺结节的灵敏度、特异度、准确率、AUC分别为89.36%(42/47)、91.18%(31/34)、90.12%(73/81)、0.904[95%CI(0.818,0.958)]，其中智能超声诊断系统诊断的AUC高于超声科医师（Z=2.673,P=0.008）。超声科医师对甲状腺功能异常患者乳腺结节的检出结果与“金标准”的一致性一般（Kappa=0.799,P<0.001），而智能超声诊断系统与“金标准”的一致性良好（Kappa=0.975,P<0.001）；混淆矩阵结果显示，超声科医师、智能超声诊断系统的假阳性例数分别为3、0例，假阴性例数分别为5、1例；校准曲线结果显示，智能超声诊断系统的诊断概率与实际概率一致性高，校准曲线与理想曲线拟合度良好（Hosmer-Lemeshow检验：χ~2=1.246,P=0.997）。结论 利用深度学习算法构建的甲状腺功能异常患者乳腺结节智能超声诊断系统具有良好的诊断效能，能够帮助超声科医师提高筛查效率和准确率。更多还原

【Abstract】 Objective To construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction using deep learning algorithms. Methods We collected breast ultrasound images of 178 patients with thyroid dysfunction(969 images) from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from January 2023 to February 2024, which served as the training set. The deep learning algorithm was used to construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction. In addition,we collected breast ultrasound images of 81 patients with thyroid dysfunction(445 images) from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from March 2024 to January 2025, which served as the validation set. The above system was used as validation set to diagnose whether patients with thyroid dysfunction had breast nodules, and the diagnostic efficacy of imaging physicians’ diagnosis and the intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction was analyzed. The consistency between the diagnosis of ultrasound physicians, intelligent ultrasound diagnosis system and the “gold standard” was tested by Kappa test. Results There was no statistically significant difference in age, type of thyroid dysfunction, disease duration, number of breast nodules, and other clinical data between the training set and the validation set(P>0.05). The time required for the training set intelligent ultrasound diagnostic system to diagnose a single breast ultrasound image was(0.04±0.01) min, which was shorter than that of an ultrasound physicians [(12.36±2.58) min], t=63.709, P<0.001. The sensitivity, specificity, accuracy,and area under the curve(AUC) of detecting breast nodules in patients with thyroid dysfunction using an intelligent ultrasound diagnostic system were 97.87%(46/47), 100%(34/34), 98.77%(80/81), and 0.997 [95%CI:(0.951, 1.00)],respectively. The sensitivity, specificity, accuracy, and AUC of detecting breast nodules by ultrasound physicians were89.36%(42/47), 91.18%(31/34), 90.12%(73/81), and 0.904 [95%CI:(0.818, 0.958)], respectively. The AUC of the intelligent ultrasound diagnosis system was higher than that of the ultrasound physician(Z=2.673, P=0.008). The detection results of breast nodules in patients with thyroid dysfunction diagnosed by ultrasound physicians were generally consistent with the “gold standard”(Kappa value=0.799, P<0.001), while the intelligent ultrasound diagnosis system was in good agreement with the “gold standard”(Kappa value=0.975, P<0.001). The confusion matrix results showed that the number of false positives was 3 and 0 for the ultrasound department physicians and the intelligent ultrasound diagnostic system, respectively, while the number of false negatives was 5 and 1. The calibration curve results indicated a high consistency between the diagnostic probability and the actual probability of the intelligent ultrasound diagnostic system,with the calibration curve fitting well with the ideal curve(Hosmer-Lemeshow test: χ~2=1.246, P=0.997). Conclusion The intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction constructed by deep learning algorithm has good diagnostic efficacy, which can help ultrasound physicians improve screening efficiency and accuracy.更多还原