Artificial intelligence-assisted scar visualization under intraoperative bleeding using CycleGAN and uncertainty fusion in laparoscopic cholecystectomy.

Abstract

Background: Accurate intraoperative identification of scar tissue is essential for preventing bile duct injury during laparoscopic cholecystectomy (LC), especially under visually impaired conditions caused by bleeding. This study aimed to develop an artificial intelligence (AI)-based framework to enhance scar region prediction in such challenging surgical environments.

Methods: A hybrid approach was proposed, combining Cycle-Consistent Generative Adversarial Network-based image translation with uncertainty-aware fusion. Bleeding-contaminated laparoscopic images were translated into pseudo non-bleeding representations using unpaired domain adaptation. Segmentation results obtained from the original and translated images were then fused based on pixel-wise entropy to improve robustness.

Results: The system was evaluated using 99 representative images from 20 surgical patients. Compared with conventional segmentation methods, the proposed framework significantly improved Dice coefficients across all three board-certified endoscopic surgeons who served as expert annotators, with all improvements demonstrating significance (P < 0.001). Subjective evaluations by the same surgeons confirmed high clinical utility, particularly in scar visibility and boundary delineation. The framework achieved near real-time inference speed (0.06 s per frame on an RTX A5000 GPU).

Conclusion: This AI-assisted framework improved the accuracy and robustness of scar tissue detection during LC, even in bleeding-compromised fields. Its real-time capability and strong clinical validation indicate substantial potential for intraoperative application and enhancement of surgical safety.