A Novel Dual-Modal Deep Learning Approach for Real-Time Removal of Hepatic Fluorescence in Indocyanine Green-Guided Laparoscopic Cholecystectomy

Indocyanine green-guided laparoscopic cholecystectomy (ICGLC) improves biliary visualization but is often hindered by hepatic fluorescence contamination, which interferes with anatomical structures. This study aims to develop and evaluate a novel dual-modal deep learning framework to automatically detect and remove hepatic fluorescence contamination in real-time during ICGLC procedures. A dataset of 33,123 dual-modality surgical frames was constructed from 48 patients who underwent elective ICGLC. Fluorescence and white-light images were fused and annotated. Several deep learning models were compared, and a DeepLabV3-based mid-fusion network was selected. Morphological post-processing and phased training strategies were implemented to enhance segmentation accuracy and generalizability. The proposed model achieved a Dice coefficient of 0.838 and recall of 0.863 on the final test set. In subjective evaluations, 10 senior surgeons consistently rated the AI-processed videos as clearer, with markedly improved bile duct visualization and reduced visual fatigue. The model demonstrated real-time performance at 0.018 seconds per frame. This study presents the first real-time AI solution for hepatic fluorescence removal in ICGLC. The dual-modal deep learning model significantly enhances visual clarity, offering potential to improve surgical safety, operational efficiency, and training effectiveness. Future prospective studies are warranted to assess the clinical impact on operative outcomes.