Korean J Radiol.  2018 Aug;19(4):665-672. 10.3348/kjr.2018.19.4.665.

Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience

  • 1Department of Radiology, Ajou University School of Medicine, Suwon 16499, Korea. radhej@naver.com
  • 2Department of Biostatistics, Ajou University School of Medicine, Suwon 16499, Korea.


To prospectively evaluate the diagnostic performance of computer-aided diagnosis (CAD) for detection of thyroid cancers via ultrasonography (US).
This study included 50 consecutive patients with 117 thyroid nodules on US during the period between June 2016 and July 2016. A radiologist performed US examinations using real-time CAD integrated into a US scanner. We compared the diagnostic performance of radiologist, the CAD system, and the CAD-assisted radiologist for the detection of thyroid cancers.
The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the CAD system were 80.0, 88.1, 83.3, 85.5, and 84.6%, respectively, and were not significantly different from those of the radiologist (p > 0.05). The CAD-assisted radiologist showed improved diagnostic sensitivity compared with the radiologist alone (92.0% vs. 84.0%, p = 0.037), while the specificity and PPV were reduced (85.1% vs. 95.5%, p = 0.005 and 82.1% vs. 93.3%, p = 0.008). The radiologist assisted by the CAD system exhibited better diagnostic sensitivity and NPV than the CAD system alone (92.0% vs. 80.0%, p = 0.009 and 93.4% vs. 88.9%, p = 0.013), while the specificities and PPVs were not significantly different (88.1% vs. 85.1%, p = 0.151 and 83.3% vs. 82.1%, p = 0.613, respectively).
The CAD system may be an adjunct to radiological intervention in the diagnosis of thyroid cancer.


Artificial intelligence; Computer-aided diagnosis; Thyroid nodule; Thyroid cancer; Ultrasonography; Ultrasound
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