Color-coded laparoscopic liver resection using artificial intelligence: A preliminary study

Area of Science:

• Surgical Technology
• Medical Imaging
• Artificial Intelligence

Background:

• Laparoscopic liver resections require precise identification of intrahepatic vascular structures.
• Minimizing risks like bleeding during surgery is crucial for patient safety.
• Real-time intraoperative guidance systems can improve surgical outcomes.

Purpose of the Study:

• To evaluate the capability of an artificial intelligence (AI) system in identifying intrahepatic vascular structures during laparoscopic liver resection.
• To assess the real-time performance of the AI system under various imaging conditions, including bleeding and indocyanine green (ICG) fluorescence.

Main Methods:

• Development and implementation of an AI system for real-time image analysis.
• Integration of the AI system with laparoscopic surgical equipment and ICG fluorescent imaging.
• Testing the AI system's accuracy in identifying vascular structures during simulated and actual surgical procedures.

Main Results:

• The AI system accurately identified intrahepatic vascular structures in real time.
• Color-coded visualization of vascular anatomy was achieved under both standard and fluorescent imaging conditions.
• The system demonstrated potential for real-time navigation support during surgery.

Conclusions:

• AI can precisely identify intrahepatic vascular structures during laparoscopic liver resection.
• The developed AI system offers real-time navigation and enhances safety in liver surgery.
• This technology holds promise for improving the precision and safety of both laparoscopic and robotic liver resections.