A Multicenter Study on Multimodal Diagnosis and Process Optimization of Thyroid Diseases Based on Ultrasonic Intelligent Agents
Trial ID: NCT07296575
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2027-12-31
Sponsor: Second Affiliated Hospital of Nanchang University
Collaborators: Zhejiang University; First Affiliated Hospital of Gannan Medical University; Ganzhou City People's Hospital; Tumor Hospital of Jiangxi Province; Chudong Medical Group Hospital (Yugan County); Zhengzhou Third People's Hospital; Xuzhou Hospital of Traditional Chinese Medicine; Yifu Hospital Affiliated to Nanjing Medical University; Fuyang people's hospital; General Hospital of Ningxia Medical University; Ji'an Central People's Hospital; Harbin First Specialized Hospital; Sun Yat-sen University; Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine; Yancheng First People's Hospital; Yangling Demonstration Zone Hospital of Shaanxi Province; Beijing Friendship Hospital; Fengfeng Mineral Bureau General Hospital of Hebei Province; Hunan Provincial Tumor Hospital; Pingdingshan first people's Hospital; PLA Joint Logistics Support Force No. 908 Hospital; Bai Cheng Central Hospital; PLA No. 901 Hospital of the Joint Support Force; The Affiliated Hospital of Jinggangshan University; Ji'an Third People's Hospital
Conditions: Thyroid Abnormalities; Thyroid; Thyroid Cancer
Interventions: No intervention
Locations: Second Affiliated Hospital of Nanchang University
Countries: China
Enrollment: 2000
Age range: None - None
Sex: ALL

Summary:
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.

Eligibility criteria:
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.

Outcomes:
Predictive Performance of Large Models in Ultrasound Thyroid Applications for Thyroid Diseases