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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Multiple instance convolutional neural network for gallbladder assessment from laparoscopic images.

Constantinos Loukas¹, Athanasios Gazis¹, Dimitrios Schizas²

¹Laboratory of Medical Physics, Medical School, National and Kapodistrian University of Athens, Athens, Greece.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|August 9, 2022

Summary

This summary is machine-generated.

This study introduces an AI framework for classifying gallbladder wall vascularity during laparoscopic cholecystectomy. The novel method achieves high accuracy, aiding surgical assessment.

Keywords:

CNN artificial intelligence deep learning gallbladder laparoscopic cholecystectomy multiple instance

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Area of Science:

Medical Imaging
Artificial Intelligence
Surgical Technology

Background:

Laparoscopic cholecystectomy (LC) requires assessing gallbladder (GB) wall vascularity.
Intraoperative imaging presents challenges for accurate vascularity classification.

Purpose of the Study:

To develop an artificial intelligence (AI) framework for classifying gallbladder (GB) wall vascularity.
To analyze intraoperative images from laparoscopic cholecystectomy (LC) procedures.

Main Methods:

A two-stage Multiple Instance Convolutional Neural Network (MICNN) was developed.
Feature extraction using a convolutional autoencoder on 4585 GB image patches.
A multi-instance encoder with pooling and self-attention mechanisms for classification.

Main Results:

The framework was evaluated on 234 GB images from 68 LC videos.
Achieved high performance in image- and video-level classification (accuracy 92.6%-93.2%, F1 93.5%-93.9%).
Performance closely matched expert evaluator agreement (94%).

Conclusions:

The AI framework offers a novel approach for classifying LC operations based on GB wall vascular patterns.
This technique can enhance surgical decision-making and assessment during LC.