YOLO11-based deep learning system for automated tubal patency classification in hysterosalpingography: a comparative...

In the United States, around 500,000 hysterosalpingography (HSG) procedures are performed annually. One fluoroscopic procedure that is frequently used to evaluate tubal patency in infertile women is hysterosalpingography. Clinical decision-making depends on the fast and accurate classification of tubal patency results, but this procedure still depends on radiologist skill, which varies greatly across clinical situations. In order to automatically classify tubal patency categories in HSG pictures, this study suggests using YOLO11, a cutting-edge deep learning architecture. Three baseline models YOLOv8n, ResNet50, and EfficientNetB0 were used to train and evaluate YOLO11s using a publicly accessible clinical dataset of 892 real HSG images annotated by three board-certified radiologists and arranged into four pathological categories: bilateral patency, bilateral blockage, bilateral partial patency, and unilateral patency. Standardized training techniques were used to conduct experiments on GPU-accelerated infrastructure. With an inference speed of 19.98 ms per image and an overall test accuracy of 76%, YOLO11s demonstrated clinically relevant performance for the Unilateral Patency class (F1-score = 0.83, recall = 0.96). YOLO11s demonstrated competitive accuracy with much fewer parameters than ResNet50 (5.4 M vs. 25.6 M), outperforming ResNet50 (75.0%) and YOLOv8n (67.12%), matching EfficientNetB0 (76.19%) within 0.2%. The main factor restricting performance on minority classes was found to be class imbalance, with Unilateral Patency accounting for 66% of training images. While the results are promising for a novel application domain, the model's failure on clinically critical minority classes (Bilateral Blockage, Bilateral Patency) means it is not yet suitable for unsupervised clinical use. The proposed system should be considered as an exploratory research baseline requiring further development, class-imbalance mitigation, and prospective clinical validation before any clinical decision support application.