This study evaluated the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system (S-Detect 1 and S-Detect 2 for thyroid) for detecting thyroid cancers. Among 218 thyroid nodules in 106 patients, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the CAD systems were 80.2%, 82.6%, 75.0%, 86.3% and 81.7%, respectively, for the S-Detect 1 and 81.4%, 68.2%, 62.5%, 84.9% and 73.4%, respectively, for the S-Detect 2. The inter-observer agreement between the CAD system and radiologist for the description of calcifications was fair (kappa = 0.336), while the final diagnosis and each ultrasonographic descriptor showed moderate to substantial agreement for the S-Detect 2. To conclude, the current CAD systems had limited specificity in the diagnosis of thyroid cancer. One of the main limitations of the S-Detect 2 was its inaccuracy in recognizing calcifications, which meant that differentiation had to be undertaken by the radiologist.
Keywords: Artificial intelligence; Computer-aided diagnosis; Thyroid cancer; Thyroid nodule; Ultrasonography.
Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.