| 摘要: |
| 目的 分析人工智能超声辅助诊断系统(AI-UADS)联合超声造影(CEU)对美国放射学会(ACR) 甲状腺影像报告和数据系统(TI-RADS)4类结节的诊断价值。方法 选取2018年3月-2022年3月在本院收治的84例ACR TI-RADS 4 类甲状腺结节患者,分别采用AI-UADS和CEU进行检查,比较两者及其联合检测对ACR TI-RADS 4类结节良恶性的诊断价值。结果 84例患者中共存在92个结节,其中恶性结节有17个,占比18.48%。与病理结果比较,CEU检出恶性结节14个,诊断甲状腺恶性结节的敏感度为82.35%,特异度为81.33%、准确度81.52%,阳性预测值为50.00%、阴性预测值为95.31%,kappa值为0.509；AI-UADS检出恶性结节15个,诊断甲状腺恶性结节的敏感度为88.24%,特异度为82.67%、准确度83.70%,阳性预测值为53.57%、阴性预测值为96.88%,kappa值为0.567；联合检测诊断甲状腺恶性结节的敏感度为82.35%,特异度为93.33%,准确度91.30%,阳性预测值为73.68%、阴性预测值为95.89%,kappa值为0.724。联合检测特异度均高于CEU和AI-UADS(χ2=0.027,0.044,P<0.05)。联合检测特异度均高于CEU和AI-UADS(χ2=0.027,0.044,P<0.05)。结论 AI-UADS和CEU对甲状腺ACR TI-RADS 4类结节良恶性诊断中均具有较好的诊断效能,联合检测可有效提高临床特异度。 |
| 关键词: 人工智能超声智能辅助系统 超声造影 甲状腺影像报告和数据系统 甲状腺结节 诊断价值 |
| DOI： |
| 投稿时间：2022-10-26修订日期：2022-10-26 |
| 基金项目: |
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| Diagnostic value of artificial intelligence ultrasonic assisted diagnosis system combined with contrast-enhanced ultrasound in ACR TI-RADS class 4 nodules |
| linshaokun,zengzhixiong,liuzhonghua,yuweifeng,guoxu |
| () |
| Abstract: |
| Objective To analyze the diagnostic value of artificial intelligence ultrasonic assisted diagnosis system (AI-UADS) combined with contrast-enhanced ultrasound (CEU) for American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) class 4 nodules. Methods A total of 84 patients with ACR TI-RADS class 4 thyroid nodules admitted to the hospital were enrolled between March 2018 and March 2022. All underwent AI-UADS and CEU examinations. The diagnostic value of the two methods and combined detection for benign and malignant ACR TI-RADS class 4 nodules was compared. Results There were 92 nodules in the 84 patients, including 17 malignant nodules (18.48%). CEU showed that there were 14 malignant nodules, its sensitivity, specificity, accuracy, positive predictive value, negative predictive value and kappa value in the diagnosis of malignant nodules were 82.35%, 81.33%, 81.52%, 50.00%, 95.31% and 0.509, respectively. AI-UADS showed that there were 15 malignant nodules, and its sensitivity, specificity, accuracy, positive predictive value, negative predictive value and kappa value were 88.24%, 82.67%, 83.70%, 53.57%, 96.88% and 0.567, respectively. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value and kappa value of combined detection were 82.35%, 93.33%, 91.30%, 73.68%, 95.89% and 0.724, respectively. The specificity of combined detection was higher than that of CEU and AI-UADS (χ2=0.027, 0.044, P<0.05). Conclusion Both AI-UADS and CEU have good diagnostic efficiency for benign and malignant ACR TI-RADS class 4 thyroid nodules, and the combined detection can effectively improve clinical specificity. |
| Key words: Artificial intelligence ultrasonic assisted diagnosis system Contrast-enhanced ultrasound Thyroid imaging reporting and data system Thyroid nodule Diagnostic value |
