Visualization of Surgical Needle Tips Hidden Inside Organs Using Generative Adversarial Networks

Area of Science:

• Medical technology
• Surgical innovation
• Minimally invasive surgery

Background:

• Postoperative complications, particularly anastomotic leakage, are common in challenging laparoscopic procedures.
• Uneven suture spacing during surgery contributes significantly to anastomotic leakage.
• Current methods rely on surgeon intuition, making uniform suture spacing difficult.

Purpose of the Study:

• To develop a model for visualizing suture needle tips within organs during surgery.
• To improve surgical accuracy and reduce the incidence of anastomotic leakage.
• To provide real-time feedback on needle placement.

Main Methods:

• Construction of a novel model to visualize the suture needle tip.
• Testing image inference capabilities of the developed model.
• Evaluation of real-time performance and estimation accuracy.

Main Results:

• The model achieved a real-time processing speed of 33.4 frames per second.
• The average estimated misalignment of the needle tip was 1.03 mm.
• This misalignment is less than 1.8 mm, representing 10% of the total needle length.

Conclusions:

• The proposed model effectively estimates the position of hidden suture needle tips.
• This visualization technology has the potential to significantly improve surgical accuracy.
• The findings suggest a reduction in anastomotic leakage and improved patient outcomes.