# Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
## 4 Discussion  
We developed a QA system for CXR imaging using DL methods. The orientation, angle, and left-right reversal of CXR were corrected completely within approximately 0.4 s. In addition, the patient's position, such as standing, sitting, and lying, was estimated with 96% accuracy. [PERSON] et al. achieved 99.4% accuracy for the orientation correction of pediatric CXR using a machine learning method [14]. In contrast, we acquired 100% accuracy for the correction of orientation, angle, and left-right reversal of CXR. This result suggested the advantage of using CNNs for the correction of CXR. [PERSON] et al. reported highly accurate correction of various parts of radiographs using CNNs. They achieved 99.3% accuracy using VGG-16 and 0.02 s of processing with the GPU [15]. Meanwhile, we proposed a specific QA system with 100% geometric correction in CXR. This result showed that it might be better to construct the QA system according to each body part. The processing time will not become a large problem for routine clinical use because under 1 s is clearly faster than manual operation by technologists. Totally, the main advantage of our study compared to the above studies is that we combined four simple models to construct the QA system. Moreover, we also estimated the patient's position directly related to the imaging findings. We believed that our study could propose a novel, comprehensive QA system for CXR based on the DL method. The constructed QA system combined classification and regression using CNN. ResNet-50 demonstrated better performance for classification, correct orientation, left-right, and patient position estimation. We have already confirmed good compatibility with classification in medical images and ResNet-50 in magnetic resonance images [21]. The same trend was confirmed in this study. On the contrary, the original CNN that had simple layers showed significantly higher performance for the regression method for correcting the angle of CXR images. We thought that the original CNN could avoid overtraining compared with other CNNs in the regression of angle correction. ResNet-50 might have too many deep layers to use the angle correction. The accuracy of the entire QA system can be assured by using the appropriate model for each task. In the detection of pneumonia in CXR using DL, [PERSON] et al. achieved extremely high accuracy, such as 97%, using modified VGG-16. This study strongly suggests that VGG-16-like CNNs are compatible with CXR and can exhibit high performance [22]. They showed the efficacy of dropouts on CNN. Our CNNs may be able to be refined according to their method. Figure 5: Confusion matrix of the overall test. QA systems should be built into the X-ray console system. CXR is immediately corrected, and the patient's position's marker is assigned. Then, radiological technologists confirm the corrected CXR and send it to the picture archiving and communication system. Recently, medical errors have been serious concerns in the clinical scene. In the United States, medical errors claim more lives in hospitals than motor vehicle accidents, breast cancer, or AIDS [10]. According to the Swiss cheese model and Heinrich's law, many minor accidents are hidden behind serious medical accidents. Therefore, the prevention of minor accidents is directly related to the prevention of major accidents. In the circumstances, artificial intelligence has been employed to develop an automatic QA system in the medical field. [PERSON] et al. summarized the current status of the QA system based on AI in radiotherapy. In the radiotherapy field, various technologies using AI have been applied. The examples are auto-segmentation, image registration, auto-planning, CT image generation, patient QA, and machine QA. They suggested the importance of the correct use of case-specific and routine QA in clinical practice [23]. [PERSON] et al. reported that machine learning approaches are explored, highlighting specific applications in machine and patient-specific quality assurance (QA).