Retaking assessment system based on the inspiratory state of chest X-ray image

TL;DR

This paper introduces a system using AI to assess whether chest X-rays need to be retaken based on the patient's breathing state, aiming to reduce human judgment variability.

Contribution

A CNN-based system is proposed to evaluate retaking necessity in chest X-rays, reducing operator variability.

Findings

The system achieved 82.3% accuracy on actual chest X-ray images.

Dynamic digital radiography was used to generate training data for the CNN.

Abstract

When taking chest X-rays, the patient is encouraged to take maximum inspiration and the radiological technologist takes the images at the appropriate time. If the image is not taken at maximum inspiration, retaking of the image is required. However, there is variation in the judgment of whether retaking is necessary between the operators. Therefore, we considered that it might be possible to reduce variation in judgment by developing a retaking assessment system that evaluates whether retaking is necessary using a convolutional neural network (CNN). To train the CNN, the input chest X-ray image and the corresponding correct label indicating whether retaking is necessary are required. However, chest X-ray images cannot distinguish whether inspiration is sufficient and does not need to be retaken, or insufficient and retaking is required. Therefore, we generated input images and labels from dynamic digital radiography (DDR) and conducted the training. Verification using 18 dynamic chest X-ray cases (5400 images) and 48 actual chest X-ray cases (96 images) showed that the VGG16-based architecture achieved an assessment accuracy of 82.3% even for actual chest X-ray images. Therefore, if the proposed method is used in hospitals, it could possibly reduce the variability in judgment between operators.

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