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Deep learning-based vessel automatic recognition for laparoscopic right hemicolectomy.

Kyoko Ryu^1,2,3, Daichi Kitaguchi^1,2, Kei Nakajima^1,2

¹Surgical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.

Surgical Endoscopy

|November 10, 2023

Summary

This summary is machine-generated.

A new deep learning model accurately identifies major blood vessels during laparoscopic right hemicolectomy (RHC) for colon cancer. This AI tool aids surgeons in visualizing critical anatomy for safer procedures and improved lymph node dissection.

Keywords:

Artificial intelligence Deep learning Laparoscopic right hemicolectomy Semantic segmentation Vessel recognition

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Area of Science:

Surgical Oncology
Medical Imaging
Artificial Intelligence

Background:

Accurate vascular anatomy recognition is crucial for laparoscopic right hemicolectomy (RHC) in right-sided colon cancer.
Challenges exist in identifying major blood vessels for lymph node dissection and surgical safety.

Purpose of the Study:

To develop and evaluate a deep learning (DL) model for automatic recognition and visualization of major blood vessels during laparoscopic RHC.

Main Methods:

A retrospective feasibility study involving semantic segmentation of superior mesenteric vein (SMV), ileocolic artery (ICA), and ileocolic vein (ICV) using a DL model.
Model performance was assessed using Dice coefficient, recall, and precision via fivefold cross-validation.
Clinical applicability was evaluated by 13 surgeons using a grading rubric.

Main Results:

The DL model achieved high accuracy for SMV recognition (metrics > 0.75).
Recognition accuracy for ICA and ICV ranged from 0.53 to 0.57.
Surgeons provided acceptable ratings for the model's clinical application potential.

Conclusions:

A DL-based vessel segmentation model was successfully developed for laparoscopic RHC.
The model demonstrates feasible identification and visualization of major blood vessels.
This tool can potentially enhance surgical navigation and safety in RHC procedures.