Multimodal ultrasound and artificial intelligence for characterization of thyroid nodules in Hashimoto's thyroiditis: current challenges and future perspectives.

Patients with Hashimoto's thyroiditis (HT) frequently present with concurrent nodular lesions such as nodular goiter and thyroid cancer (especially papillary thyroid carcinoma, PTC), and their risk of PTC is significantly higher than that of non-HT individuals. Patients with HT exhibit varying degrees of glandular fibrosis, leading to some areas having a "nodular-like" appearance. Some of these nodules may display features suggestive of malignancy, which can be difficult to distinguish using conventional ultrasound. Multimodal ultrasound combined with fine-needle aspiration biopsy (FNAB) has emerged as the most effective method currently for identifying the benign or malignant nature of nodules in the context of HT, owing to its advantages of cost-effectiveness, convenience, and reproducibility. Artificial intelligence (AI) has been increasingly applied in the medical field, but its accuracy in diagnosing HT-associated thyroid nodules (TNs) still requires further refinement. This article reviews the progress of multimodal ultrasound technology combined with AI in assessing and diagnosing the benign and malignant nature of TNs in patients with HT.