Clinical Application of AI-assisted Ultrasound Technology in the Preoperative Evaluation of Thyroid Cancer

This study aims to explore the application of AI-assisted ultrasound technology in the preoperative assessment of thyroid cancer. Traditional ultrasound examination data from thyroid cancer patients will be collected, and AI systems will be utilized to detect and diagnose thyroid nodules and lymph nodes. In cases where there is disagreement between the two-dimensional ultrasound and AI system results, further confirmation will be sought through biopsy. Subsequently, pathological results will serve as the "gold standard" for comparison between the AI system and traditional ultrasound examination results, assessing their accuracy and reliability. Through this research endeavor, a more accurate and reliable method for preoperative assessment of thyroid cancer is aspired to be offered, thereby supporting clinical decision-making and paving the way for novel applications of AI in the field of medical imaging diagnosis.

This study aims to investigate the application of AI-assisted ultrasound technology in the preoperative assessment of thyroid cancer. Traditional ultrasound examination data from patients with thyroid cancer, including two-dimensional ultrasound images, color Doppler flow images, and detailed characteristics of thyroid nodules and lymph nodes such as number, size, morphology, echogenicity, margins, calcifications, and aspect ratio, will be collected. Prior to surgery, a reassessment will be conducted using AI-assisted ultrasound technology, and the detection and diagnostic results of thyroid nodules and lymph nodes by the AI system will be recorded. In cases where there is discrepancy between the results of two-dimensional ultrasound and the AI system, fine needle aspiration biopsy or intraoperative biopsy will be performed for further confirmation of their nature. Post-surgery, the pathological results of each nodule will serve as the "gold standard" for comparative analysis between the AI system and traditional two-dimensional ultrasound examinations. The accuracy of the AI system in detecting and localizing nodules will be analyzed, and its sensitivity, specificity, and accuracy will be calculated to evaluate its diagnostic efficacy and reliability in the preoperative assessment of thyroid cancer. Through this research, a more accurate and reliable adjunctive diagnostic method for the preoperative assessment of thyroid cancer is aimed to be provided to assist clinical decision-making. Additionally, new avenues and directions for the application of AI in the field of medical imaging diagnosis will be explored.

Eligibility Criteria:
Inclusion Criteria:

* Patients with preoperative pathological confirmation of thyroid malignant tumors undergoing surgical treatment.
* Patients with benign thyroid tumors, such as thyroid adenomas causing compressive symptoms, undergoing surgical treatment.
* Patients with complete and high-quality traditional two-dimensional color ultrasound images.
* Complete postoperative pathology reports.
* Willingness to participate in this clinical trial and signing of informed consent.

Exclusion Criteria:

* Patients with a history of neck surgery or radiotherapy.
* Patients with a history of malignant tumors in other parts of the body.
* Patients with thyroid dysfunction.
* Incomplete or poor-quality traditional two-dimensional color ultrasound images.
* Incomplete postoperative pathology reports.
* Refusal to participate in this clinical trial.