Development and clinical evaluation of an AI-assisted respiratory state classification system for chest X-rays: A...

In this study, we aimed to develop and clinically evaluate an artificial intelligence (AI)-assisted support system for determining inhalation and exhalation states on chest X-ray images, focusing on the specific challenge of respiratory state determination.

We developed a body mass index (BMI)-specific approach for respiratory state classification in chest X-rays using separate models for normal and obesity groups. Feature extraction was performed using four pre-trained networks (EfficientNet B0, GoogleNet, Xception, and VGG16) combined with Naive Bayes classification. A database of 3200 chest X-ray images from 1600 patients, labeled for respiratory states using temporal subtraction techniques, was utilized. The system's clinical utility was assessed through an observational study involving eight radiological technologists with varying experience levels.

The approach combining EfficientNet B0 late-layer with Naive Bayes classification and GoogleNet's end-to-end model demonstrated the highest performance. The support system significantly improved the area under the curve from 0.728 to 0.796 in the normal BMI group and from 0.752 to 0.817 in the obesity group (p < 0.05), showing particular effectiveness in classifying exhalation states in obese patients.

The developed AI-assisted support system enhances radiological technologists' ability to determine respiratory states across varying levels of experience, particularly in challenging cases involving obese patients. This system contributes to improving image quality assessment and workflow efficiency by potentially reducing unnecessary re-imaging.