# Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
## 1 Introduction  
Chest X-ray (CXR) imaging has been used for diagnostic imaging since the beginning, and despite the development of diagnostic imaging equipment such as computed tomography (CT) and magnetic resonance imaging (MRI), CXR imaging has still been used for screening for cardiovascular and pulmonary diseases. The cardiothoracic ratio (CTR) measurement using CXR images is an important index to evaluate heart diseases [1, 2, 3]. CTR is usually calculated as the ratio of the largest transverse heart dimension to the largest transverse chest dimension in a CXR obtained from posterior irradiation. CXR with posterior irradiation can avoid the enlargement of the cardiac shadow caused by the proximity of the detector and heart, based on the geometry of the X-ray system. Lung water also works as an important index to assess heart and lung diseases and can be evaluated in lung water secondary to increased intravascular hydrostatic pressure due to cardiogenic edema resulting from heart failure, increased permeability of pulmonary microvessels due to acute lung injury or acute respiratory distress syndrome, and other causes [4, 5]. Since the beginning of the coronavirus disease-2019 (COVID-19) pandemic, CXR imaging has played a crucial role in the evaluation of lung conditions. It is easily accessed, has a low radiation exposure as a primary screening, and a low cost to assess pneumonia, which is one of the typical symptoms of COVID-19 [6, 7, 8]. The importance of the CXR examination in daily medicine is undoubtedly important. However, during CXR imaging, both CTR and the distribution of lung water change with the patient's position. For example, if the patient is able to walk independently, CXR imaging is performed in the standing position; therefore,if lung water is detected, it is concentrated in the lower lung field due to the gravitational effect. However, in a patient with difficulty standing, such as a critically ill patient, CXR imaging is performed in the supine position, and therefore, if lung water is present, it is distributed over the entire dorsal side of the lungs. In other words, the patient's position is an important factor in the accurate assessment of CXR images in clinical practice. Therefore, the patient's position should be reported to readers immediately after CXR imaging. However, most of these works depend on manual operations by radiological technologists. For example, technologists add a marker that shows the patient's position, such as \"standing\", \"sitting\", and \"lying\", to the free space in the CXR image immediately after CXR imaging. Even though these markers allow the image reader, the radiologist, and the physician in the medical department to identify the imaging status at a glance for a large number of taken clinical images, human errors may occur at some point during this process, and these errors may cause serious incidents by misunderstanding the patient's position in clinical practice [9]. For instance, imaging findings will be confused by a misunderstanding of the patient's position. Moreover, CXR output with left-right reversal is due to the difference in the irradiation direction due to incorrect imaging parameters of the X-ray console between a standing position and another position. Despite the large efforts of radiological technologists, these are quite often encountered in clinical practice. Therefore, an automatic and highly accurate system without manual operation is desired. Artificial intelligence (AI) has been applied for the automatic assessment of medical images in recent years [10; 11]. Amount of image data is needed to construct a high-accuracy model in machine learning (ML) and deep learning (DL). From this view, CXR, which has been traditional and common, may be suitable for attempts to apply ML and DL methods [12]. Meanwhile, [PERSON] et al.