Abstract
BACKGROUND: Thyroid nodules are a common clinical concern, with accurate diagnosis being critical for effective treatment and improved patient outcomes. Traditional ultrasound examinations rely heavily on the physician's experience, which can lead to diagnostic variability. The integration of artificial intelligence (AI) into medical imaging offers a promising solution for enhancing diagnostic accuracy and efficiency. This study aimed to evaluate the effectiveness of the You Only Look Once v. 11 (YOLOv11) model in detecting and classifying thyroid nodules through ultrasound images, with the goal of supporting real-time clinical decision-making and improving diagnostic workflows. METHODS: We used the YOLOv11 model to analyze a dataset of 1,503 thyroid ultrasound images, divided into training (1,203 images), validation (150 images), and test (150 images) sets, comprising 742 benign and 778 malignant nodules. Advanced data augmentation and transfer learning techniques were applied to optimize model performance. Comparative analysis was conducted with other YOLO variants (YOLOv3 to YOLOv10) and residual network 50 (ResNet50) to assess their diagnostic capabilities. RESULTS: The YOLOv11 model exhibited superior performance in thyroid nodule detection as compared to other YOLO variants (from YOLOv3 to YOLOv10) and ResNet50. At an intersection over union (IoU) of 0.5, YOLOv11 achieved a precision (P) of 0.841 and recall (R) of 0.823, outperforming ResNet50's P of 0.8333 and R of 0.8025. Among the YOLO variants, YOLOv11 consistently achieved the highest P and R values. For benign nodules, YOLOv11 obtained a P of 0.835 and R of 0.833, while for malignant nodules, it reached a P of 0.846 and a R of 0.813. Within the YOLOv11 model itself, performance varied across different IoU thresholds (0.25, 0.5, 0.7, and 0.9). Lower IoU thresholds generally resulted in better performance metrics, with P and R values decreasing as the IoU threshold increased. CONCLUSIONS: YOLOv11 proved to be a powerful tool for thyroid nodule detection and malignancy classification, offering high P and real-time performance. These attributes are vital for dynamic ultrasound examinations and enhancing diagnostic efficiency. Future research will focus on expanding datasets and validating the model's clinical utility in real-time settings.