Ultrasound Large-scale Model in Thyroid Field: A Multi-center Study on Medical-Engineering Integration

A Multicenter Study on Multimodal Diagnosis and Process Optimization of Thyroid Diseases Based on Ultrasonic Intelligent Agents

The goal of this observational study is to evaluate the diagnostic accuracy and clinical workflow integration of an ultrasound intelligent agent (UIA) for thyroid disease management in a real-world multicenter setting. The primary research question is:

Can the UIA improve diagnostic consistency and efficiency for thyroid nodules (TI-RADS 1-5), Hashimoto's thyroiditis, and cervical lymph node metastasis compared to traditional ultrasound interpretation? Participants will include adults (18-80 years) undergoing thyroid ultrasound at 16 participating hospitals across China. Key inclusion criteria cover patients with suspected thyroid disorders requiring imaging, while exclusion criteria address poor image quality or concurrent clinical trials. Over 2,000 cases (50% thyroid nodules, 30% diffuse lesions, 12.5% non-nodular abnormalities, 7.5% special populations) will be prospectively enrolled. Data collection integrates static/dynamic ultrasound images, laboratory results, and AI-generated reports. Primary endpoints include model performance metrics (AUC, sensitivity/specificity, TI-RADS Kappa ≥0.8), workflow efficiency (report generation time ≤5 minutes), and pediatric/pregnancy-specific reference standards. Secondary analyses will assess inter-rater reliability (Cohen's Kappa) and longitudinal outcomes via 6-12-month follow-up. This study aims to establish evidence-based guidelines for AI-augmented thyroid diagnosis, particularly in underserved regions, while addressing gaps in current AI validation frameworks related to multi-modality data fusion and special population adaptability.

Inclusion Criteria:

• Ages 18-80, clinically suspected thyroid disease (e.g., enlargement, nodules, pain) requiring ultrasound examination;
• Diffuse lesions must demonstrate both ultrasound characteristics and laboratory evidence;
• Dynamic video must fully cover the maximum diameter of the nodule without significant probe movement;
• Voluntary signed informed consent.

Exclusion Criteria:

• Poor image quality (severe gas interference, artifacts obscuring structural visualization);
• Inability to cooperate with examination (consciousness impairment, extreme non-compliance);
• Prior participation in other thyroid ultrasound-related clinical trials; postoperative thyroid recurrence;
• Thyroid malformation/ectopia affecting visualization.