Real-Time Image Segmentation, Tracking, and Augmented Reality Overlays to Improve Surgical Precision and Reduce Complications
DOI:
https://doi.org/10.70742/ijmhn.v1i1.576Keywords:
Augmented reality, deep learning, surgical navigation, image segmentation, real-time tracking, laparoscopic surgery, precision medicine.Abstract
Advances in real-time image segmentation and augmented reality (AR) technologies are transforming modern surgical practice by improving precision, reducing complications, and enhancing patient outcomes. This study empirically examines the integration of deep learning–based segmentation, object tracking, and AR overlay visualization for intraoperative guidance in laparoscopic surgery. A hybrid system combining U-Net++ segmentation, YOLOv8–DeepSORT tracking, and Unity-based AR rendering was implemented using MATLAB and Python environments. A dataset comprising 450 laparoscopic video sequences from 50 patients was analyzed to evaluate the framework’s performance. The model achieved a segmentation accuracy of 96.8%, an Intersection over Union (IoU) of 94.2%, and a mean latency of 85 ms. Statistically significant improvements (p < .05) were observed in surgical instrument localization, tissue differentiation, and navigation efficiency. Moreover, complication rates were reduced by 22.4% compared to conventional image-guided methods. Qualitative assessments indicated that surgeons experienced improved confidence, lower cognitive stress, and clearer visualization under dynamic intraoperative conditions. The findings suggest that integrating AI-powered segmentation, tracking, and AR visualization can enable safer, faster, and more precise surgical interventions. The proposed framework represents a scalable and clinically feasible pathway toward the realization of AI-assisted precision surgery and enhanced intraoperative decision-making.
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