# Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
## 4 Discussion
They introduced the clinical usefulness and limitations of machine learning in QA systems. They argued for the necessity of a sanity check and a second check before the clinical use of QA systems based on AI. Additionally, they suggested that we should address the limitations of both data and ML models. [24] Thus, we believe that we should avoid total reliance on AI-based QA systems, especially in the field of radiography, where AI-based QA systems have not been developed in comparison with radiotherapy. In this sense, our QA system for CXR will work as a powerful tool to perform double-checks with radiological technologists. A large amount of radiography has been taken in daily practice. Therefore, the QA system for radiographs is sure to make a significant contribution to daily clinical practice. However, many studies of radiographs using AI technology have been performed to diagnose using the classification method and to estimate the quantitative value using the regression method. We considered that these techniques should be applied to the construction of a QA system for radiographs. However, no study has reported an automatic QA system for CXR imaging to automatically correct the orientation and angle and estimate the patient's position. Hence, we attempted to develop a QA system for CXR, which is the most common radiograph in clinical practice. One of the reasons CXR was selected was that a large dataset such as Chest-14, which had over one hundred thousand CXR was available, and many clinical images were also stored on our institution's server. To create a DL model with high accuracy, a large data set is necessary. In addition, the CXR has only one pattern, which the chest is in the center of the image, and the pattern of the image is extremely small compared to other parts, such as the extremities. We did not need to prepare images of many patterns. This fact works as an advantage in the preparation of datasets and the model training process. Therefore, CXR was suitable to attempt to construct a QA system based on AI as a novel study. We excluded images with the same patients from Chest-14 to avoid training similar images. Many images became unusable, but this process was necessary to avoid overfitting specific images. It seems that the dataset had sufficient size for this task because complete correction was achieved in the orientation, angle, and left-right reversal. Chest-14 does not provide information relating detail patient's position when taking XR, such as standing, sitting, or lying. Therefore, we used the clinical images of our institution as a dataset to train the estimation of the patient's position, even though dataset was small compared with other datasets. As a result, we obtained 96% accuracy in the patient's position estimation. We believe that the data set size was sufficient for this study. Meanwhile, a few images were confused between the sitting position and the lying position in this system. This is because strict positioning management was not enforced. In addition, in both the sitting and lying positions, X-rays are irradiated from the front side of the patient. Therefore, CXR images in both positions may become very similar. From the clinical perspective, the degree of body raising is adjusted according to the patient'srespiratory status. We think that the blurring of the boundary between the sitting position and the lying position is, to some extent, unavoidable. On the other hand, the standing position was completely classified. Thus, the direction of X-ray irradiation was fully distinguished. The direction of X-ray irradiation is directly related to CXR findings, such as the distribution of pleural effusions, air inclusions and the measurement of CTR. Therefore, an accurate estimation of the irradiation direction is an important factor in CXR management in clinical practice. Moreover, orientation, image angle, and left-right reversal were fully corrected by the QA system. The processing time is clearly faster than a manual operation. These facts proved the usefulness of the QA system.