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

One fluoroscopic procedure that is frequently used to evaluate tubal patency in infertile women is hysterosalpingography.

In order to automatically classify tubal patency categories in hysterosalpingography pictures, this study suggests using YOLO11, a cutting-edge deep learning architecture.

Standardized training techniques were used to conduct experiments on GPU-accelerated infrastructure.

The main factor restricting performance on minority classes was found to be class imbalance, with Unilateral Patency accounting for 66% of training images.