# Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques
###### Abstract  
Although the widespread use of digital imaging has enabled real-time image display, images in chest X-ray examinations can be confirmed by the radiologist's eyes. Considering the development of deep learning (DL) technology, its application will make it possible to immediately determine the need for a retake, which is expected to further improve examination throughput. In this study, we developed software for evaluating chest X-ray images to determine whether a repeat radiographic examination is necessary, based on the combined application of DL technologies, and evaluated its accuracy. The target population was 4809 chest images from a public database. Three classification models (CLMs) for lung field defects, obstacle shadows, and the location of obstacle shadows and a semantic segmentation model (SSM) for the lung field regions were developed using a fivefold cross validation. The CLM was evaluated using the overall accuracy in the confusion matrix, the SSM was evaluated using the mean intersection over union (mIoU), and the DL technology-combined software was evaluated using the total response time on this software (RT) per image for each model. The results of each CLM with respect to lung field defects, obstacle shadows, and obstacle shadow location were 89.8%, 91.7%, and 91.2%, respectively. The mIoU of the SSM was 0.920, and the software RT was 3.64 \(\times\) 10\({}^{-2}\) s. These results indicate that the software can immediately and accurately determine whether a chest image needs to be re-scanned. deep learning; chest X-ray image; quality assurance system +
Footnote †: journal: Medical Science and Engineering, Faculty of Health Sciences, Hokkaido University, Sapporo 0608648, Japan
In these X-ray examinations, the target area is required to be accurately visualized, and when it cannot be visualized, retaking is necessary, which contributes to an increase in examination time and radiation exposure experienced by the subject. The study by [PERSON] et al. [3] reported the factors that cause retakes in general X-ray examinations, including CXR examination, and most of them were positioning errors, defects of necessary parts, and artifacts. Although the widespread use of digital imaging has enabled real-time image display, the radiologist's eyes are still used to determine the need for a retake. Therefore, to further improve the efficiency and accuracy of X-ray examinations, it is essential to have a system that can immediately determine such needs. In recent years, several studies have performed medical image analyses using the deep learning (DL) techniques [4; 5; 6]. Among the DL techniques using convolutional neural network, classification [7], semantic segmentation [8], and object detection [9] are suitable for medical image analyses, and there are many reports on these techniques. In the field of CXR images, the classification, semantic segmentation, and object detection technologies have been used for lesion classification [10], the semantic segmentation of the lung field areas [11; 12], and the detection of diseases in lung field areas [13], respectively. In addition, with the recent global coronavirus disease 2019 pandemic, these technologies can be applied to detect the presence of pneumonia and segment the pneumonia in the lung field area [14; 15]. The development of these technologies has led to the development of the technology. Although these technologies have been improving, their use in determining the need for a retake has not yet been fully elucidated. Konica Minolta, Inc., has already commercialized the AeroDR solution to improve the efficiency of medical examinations, and its CS-7 console is equipped with functions to detect lung field defects and body motion in the frontal CXR images [16].