Clinical Implication of the New AI-TIRADS Classification of Thyroid Nodules; Our Real Clinical Experience

Background: ultrasound and fine needle aspiration biopsy (fnab) play a primary role in determining the nature of nodules. objectives: the aim of our study is to determine the diagnostic accuracy, sensitivity, and specificity of artificial intelligencethyroid imaging reporting and data system (ai-tirads) in differentiating malignant from benign nodules to establish the treatment plan for patients and avoid unnecessary fnab. methods: this cross-sectional study was conducted between september 2020 and september 2021 (one year). patients (n = 133) who had an indication for surgery due to nodular thyroid disease and were referred to the therapeutic training center of 5 azar and falsafi private section hospitals, gorgan, iran, were included. the diagnostic accuracy of fnab and ai-tirads was calculated. results: the mean age of patients with malignant pathology (39.65 ± 11.69) was lower than that of patients with benign pathology (44.14 ± 13.54), which was statistically significant (p = 0.042). fifty-two out of 72 nodules less than 25 mm were malignant, demonstrating a higher prevalence of benign pathology in larger nodules. a pooled analysis of thyroid reporting (tr) = 2 and tr points ≥ 7 for their power in predicting the final pathology outcome showed figures of 0.97, 0.93, 0.97, and 0.97 for sensitivity, specificity, positive predictive value (ppv), and negative predictive value (npv), respectively, with an overall pooled accuracy of 0.97 for these two groups of sonographic results. conclusions: we concluded that ai-tirads has a high sensitivity and diagnostic accuracy in differentiating benign from suspicious nodules.