Journal
ULTRASOUND IN MEDICINE AND BIOLOGY
Volume 45, Issue 10, Pages 2672-2678Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ultrasmedbio.2019.05.032
Keywords
Artificial intelligence; Computer-aided diagnosis; Thyroid nodule; Thyroid cancer; Ultrasonography
Funding
- National Research Foundation of Korea, South Korea [2017 R1 C1 B5016217]
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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. (C) 2019 World Federation for Ultrasound in Medicine & Biology. All rights reserved.
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