Applying Deep Learning for Occluded Uterus and Fallopian Tube Detection for Laparoscopic Tubal Sterilization

Laparoscopic tubal sterilization is a widely used method for permanent contraception, where accurately identifying the uterus and fallopian tubes is crucial. However, this process becomes more difficult when these structures are obscured or partially hidden during surgery. Conventional imaging techniques often struggle to accurately identify anatomical features in such scenarios. In contrast, recent progress in deep learning, particularly with the You Only Look Once (YOLO) algorithm, has shown promising results by enabling real-time object detection in medical imaging. By incorporating KeyPoint detection with the newest YOLOv10 version, the system’s ability to detect and pinpoint anatomical landmarks is significantly improved, even when occlusions are present. This study presents a novel approach that merges YOLOv10 with KeyPoint detection to reliably identify the uterus and fallopian tubes in real-time laparoscopic footage, even when these structures are partially hidden. The method also identifies the Ampulla and Isthmus at the Uterine-Tubal junction, even in cases where visualization is obstructed. Additionally, an ellipse estimation technique is applied to further enhance detection under occluded conditions by estimating the uterus’s position and orientation when it is not fully visible. The results highlight the potential for real-time application of this system in clinical settings, marking a step forward in AI-assisted laparoscopic surgery.