Optimizing Thyroid Nodule Management With Artificial Intelligence: Multicenter Retrospective Study on Reducing Unnecessary Fine Needle Aspirations

作者：Jianqi Ni, Y. Liu, Chao Chen, Yi-Lei Shi, Xing Zhao, Xiao‐Long Li, Beibei Ye, Jingliang Hu, Lichao Mou, Liping Sun, Hui‐Jun Fu, Xiao Xiang Zhu, Yi-Feng Zhang, Le‐Hang Guo, Hui‐Xiong Xu · 发表于：JMIR Medical Informatics · 年份：2025 · DOI：10.2196/71740 · 被引用次数：4 · 研究领域：Thyroid Cancer Diagnosis and Treatment、AI in cancer detection、Artificial Intelligence in Healthcare and Education

Background: Most artificial intelligence (AI) models for thyroid nodules are designed to screen for malignancy to guide further interventions; however, these models have not yet been fully implemented in clinical practice. Objective: This study aimed to evaluate AI in real clinical settings for identifying potentially benign thyroid nodules initially deemed to be at risk for malignancy by radiologists, reducing unnecessary fine needle aspiration (FNA) and optimizing management. Methods: We retrospectively collected a validation cohort of thyroid nodules that had undergone FNA. These nodules were initially assessed as "suspicious for malignancy" by radiologists based on ultrasound features, following standard clinical practice, which prompted further FNA procedures. Ultrasound images of these nodules were re-evaluated using a deep learning-based AI system, and its diagnostic performance was assessed in terms of correct identification of benign nodules and error identification of malignant nodules. Performance metrics such as sensitivity, specificity, and the area under the receiver operating characteristic curve were calculated. In addition, a separate comparison cohort was retrospectively assembled to compare the AI system's ability to correctly identify benign thyroid nodules with that of radiologists. Results: The validation cohort comprised 4572 thyroid nodules (benign: n=3134, 68.5%; malignant: n=1438, 31.5%). AI correctly identified 2719 (86.8% among benign nodules) and ...