Diagnostic value of artificial intelligence ultrasonic assisted diagnosis system combined with contrast-enhanced ultrasound in ACR TI-RADS class 4 nodules

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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

更新时间：2023-10-11

- Diagnostic value of artificial intelligence ultrasonic assisted diagnosis system combined with contrast-enhanced ultrasound in ACR TI-RADS class 4 nodules
- Journal of Clinical Ultrasound in Medicine Vol. 25, Issue 2, (2023)
- 作者机构：
  
  福建医科大学附属泉州市第一医院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2023，
  
  Published Online：6 March 2023，
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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. [J]. Journal of Clinical Ultrasound in Medicine 25(2).(2023)
DOI：

Diagnostic value of artificial intelligence ultrasonic assisted diagnosis system combined with contrast-enhanced ultrasound in ACR TI-RADS class 4 nodules. [J]. Journal of Clinical Ultrasound in Medicine 25(2).(2023) DOI：

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。

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.

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

Keywords

Artificial intelligence ultrasonic assisted diagnosis systemContrast-enhanced ultrasoundThyroid imaging reporting and data systemThyroid noduleDiagnostic value

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