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

Summary

This protocol presents a dual-modal deep learning approach for real-time hepatic fluorescence removal during indocyanine green-guided laparoscopic cholecystectomy (ICGLC).

Abstract

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.

Introduction

Cholelithiasis is a prevalent gastrointestinal disorder, consistently showing high incidence rates globally across various populations, affecting over 4% of the global population^1,2,3,4. Laparoscopic cholecystectomy (LC) is extensively regarded as the 'gold standard' for treating cholelithiasis due to its advantages such as minimal intraoperative trauma, mild postoperative pain, favorable cosmetic results, and short hospital stays^5,6,7. In t....

Protocol

All surgical videos included in this study were obtained from patients who underwent elective ICG-guided laparoscopic cholecystectomy (ICGLC) at Peking Union Medical College Hospital. The study was approved by the relevant ethics committee, and informed consent was obtained from all participants before enrollment.

1. Data preparation and image annotation

1. Retrospectively collect surgical videos from 48 patients who underwent ICGLC at Peking Union Medical College Hospital between 2022 and 2024.
   NOTE: The patients included in this study were intravenously injected with 0.5 mg of ICG 20 min before surgery.
....

Results

In our study, a total of 48 patients who underwent elective indocyanine green laparoscopic cholecystectomy (ICGLC) between 2022 and 2024 in the Department of General Surgery at Peking Union Medical College Hospital were retrospectively enrolled. All patients received an intravenous injection of 0.5 mg of indocyanine green 20 min prior to surgery. Postoperative pathological examination confirmed that all cases were diagnosed with calculous cholecystitis.

After manual segmentation and selection,.......

Discussion

This research leveraged AI deep learning to address a key challenge in ICGLC surgeries: identifying and masking hepatic fluorescence contamination. Surgeons using NIRF often report that liver fluorescence causes visual fatigue and complicates biliary imaging³⁸. By applying computer vision technology, this study aimed to mitigate these issues and potentially reduce the incidence of BDI, a common complication in ICGLC. The model's inference time for processing a single frame is approximately 0.0.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
4K Fluorescence Imaging Console	Hangzhou Kangji Medical instrument Co. Ltd.	KJ-EP4K-01	Provides three imgaging modes: white light, fluorescence, and fused fluorescence; Support expansion of AI modules
4K Fluorescence Camera	Hangzhou Kangji Medical instrument Co. Ltd.	KJ-EC-4K	Fully digitalized camera with multi-function button settings; Support video recording, photography, and fluorescence mode switching
Cold Light Source System	Hangzhou Kangji Medical instrument Co. Ltd.	KJ-CLS-1	LED cold light source and fluorescenct LED light source with a fluorescence wavelength of 808 nm
NVIDIA A100 Tensor Core	NVIDIA Corporation	900-21001-0020-100	GPU