Clinical value of artificial intelligence ultrasound-assisted diagnostic system in the differential diagnosis of benign and malignant thyroid nodules
Meng Tian, Guangxi Liang, Xingwang Zhu, Yuetian Zhang, Haoyuan Zuo, Banggao Ni
Accurately distinguishing the benign thyroid nodules (BTNs) and malignant thyroid nodules (MTNs) is crucial for treatment planning and prognosis. This study aims to explore the clinical value of artificial intelligence ultrasound-assisted diagnostic system (AI-UADS) in diagnosing the BTNs and MTNs. In this retrospective case-control study, a total of 161 TNs from 91 patients confirmed by surgical pathology were included. All nodules underwent ultrasound examinations and were evaluated by both senior ultrasonographers and AI-UADS, independently. Using surgical pathology as the gold standard, the diagnostic performance of the 2 methods was analyzed using receiver operating characteristic curves (including classify the nodules based on size, location, capsule relationships, and presence or absence of Hashimoto thyroiditis [HT]). The area under the curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of AI-UADS in the differential diagnosis of BTNs and MTNs were 0.933, 93.2%, 92.4%, 94.2%, 95.5%, and 90.3%, respectively, all of which were higher than ultrasonographers (0.821, 82.0%, 81.5%, 82.6%, 86.2%, and 77.0%, respectively) (all