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Related Experiment Video

Updated: Nov 26, 2025

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Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery.

Sang-Ho Park1, Hee-Min Park1, Kwang-Ryul Baek1

  • 1Department of Electronic Engineering, Pusan National University, Busan 46241, South Korea.

World Journal of Gastroenterology
|December 14, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence-based real-time microperfusion (AIRAM) analysis accurately predicts anastomotic complications in laparoscopic colorectal surgery. This AI method offers improved consistency over conventional parameters for assessing colonic perfusion status.

Keywords:
Anastomotic complicationsArtificial intelligentColorectal surgeryIndocyanine greenLaparoscopicMicrocirculation analysis

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Area of Science:

  • Surgical Innovation
  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Colonic perfusion assessment using indocyanine green (ICG) angiography is crucial for predicting anastomotic complications in laparoscopic colorectal surgery.
  • Current quantitative parameter-based analyses show variability due to anatomical differences.
  • Artificial intelligence (AI)-based real-time analysis of microperfusion (AIRAM) offers a potential solution for improved accuracy and consistency.

Purpose of the Study:

  • To evaluate the feasibility of AIRAM in predicting anastomotic complication risk.
  • To assess the performance of AIRAM in laparoscopic colorectal cancer surgery patients.

Main Methods:

  • Indocyanine green (ICG) curves were extracted from video regions of interest during surgery.
  • Pre-processing using MATLAB reduced performance degradation from environmental factors.
  • AI learning utilized a self-organizing map (SOM) network trained on 25 ICG curve patterns from 50 patients, with validation on 15 patients.

Main Results:

  • AIRAM demonstrated consistent accuracy, especially when conventional parameters (T1/2, TR, RS) faltered with complex ICG patterns.
  • AI-based classification maintained accuracy where conventional methods decreased.
  • AI analysis showed improved statistical performance and higher F1 scores compared to conventional parameters, with a processing time suitable for real-time application.

Conclusions:

  • AI-based real-time microcirculation analysis (AIRAM) provides more accurate and consistent predictions of anastomotic complications.
  • AIRAM surpasses conventional parameter-based methods in reliability for laparoscopic colorectal surgery.