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The aim of this study was to evaluate the performance of artificial intelligence (AI) technology in the diagnosis of thyroid nodules, specifically in the field of ultrasound image analysis. It focuses on the accuracy and clinical feasibility of the AI system based on the Vision-LSTM model in the diagnosis of TI-RADS category 4b thyroid nodules.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| maligant | patients with maligant thyroid masses who underwent biopsy and/or surgical resection. | ||
| benign | patients with benign thyroid masses who underwent biopsy and/or surgical resection. |
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| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of diagnostic models | The study collected ultrasound imaging data from 401 cases of TI-RADS 4b thyroid nodules at our hospital and used this data to train and validate the Vision-LSTM model. The diagnostic results of the AI model were compared with those of junior and senior clinicians to evaluate its performance in terms of diagnostic accuracy and stability; model performance was quantified using metrics such as the area under the curve (AUC) and the precision-recall curve (PR curve). | Immediately evaluated after the diagnostic model was built |
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Inclusion Criteria:(1) Patients with thyroid nodules visible on ultrasound who underwent biopsy and/or surgical resection. (2) Diagnosed as TI-RADS category 4b on the basis of preoperative ultrasound images by two sonographers with more than 5 years of experience in thyroid ultrasound diagnosis. (3) All nodules underwent puncture biopsy or surgery to obtain pathologic results.
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Exclusion Criteria:(1)The quality of the patient's ultrasound images was poor. (2) The patient has incomplete clinical and imaging data. (3) The patient has had thyroid surgery or other treatment.
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Patients with thyroid nodules who were seen at the First Affiliated Hospital of Shandong First Medical University from January 2022 to December 2024
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| QianfoshanH | Jinan | Shandong | China |
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| ID | Term |
|---|---|
| D013964 | Thyroid Neoplasms |
| ID | Term |
|---|---|
| D004701 | Endocrine Gland Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D006258 | Head and Neck Neoplasms |
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| D004700 |
| Endocrine System Diseases |
| D013959 | Thyroid Diseases |