This retrospective study focuses on benign and malignant classification of thyroid nodules using deep learning techniques and evaluates the value of deep learning based nomograms in the classification of TI-RADS category 4 thyroid nodules to improve the accuracy of benign and malignant identification of TI-RADS category 4 thyroid nodules. Materials and methods: Patients who visited in The First Affiliated Hospital of Shandong First Medical University ＆ Shandong Provincial Qianfoshan Hospital were collected. Their general clinical features, information on preoperative ultrasound diagnosis, and postoperative pathologic data were reviewed.
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deep learning prediction model（YOLOv3） and the model evaluation
Timeframe: Immediately evaluated after the prediction model was built
nomogram prediction and assessment
Timeframe: Immediately evaluated after the nomogram was built
Selection of clinical features and assessment
Timeframe: After the dataset is collected and pathology results are obtained, the statistical results obtained are analyzed for clinical factors, averaging about 1 year.
Impact and assessment of ultrasound grading
Timeframe: The graded results of the ultrasound examination were analyzed after the data set collection was completed, the ultrasound examination was completed and the final pathology results were obtained, on average about 1 year.