Background: Chest X-ray (CXR) imaging is the most common examination; however, no automatic quality assurance (QA) system using deep learning (DL) has been established for CXR. This study aimed to construct a DL-based QA system and assess its usefulness. Method: Datasets were created using over 23,000 images from Chest-14 and clinical images. The QA system consisted of three classification models and one regression model. The classification method was used for the correction of image orientation, left–right reversal, and estimating the patient’s position, such as standing, sitting, and lying. The regression method was used for the correction of the image angle. ResNet-50, VGG-16, and the original convolutional neural network (CNN) were compared under five cross-fold evaluations. The overall accuracy of the QA system was tested using clinical images. The mean correction time of the QA system was measured. Result: ResNet-50 demonstrated higher performance in the classification. The original CNN was preferred in the regression. The orientation, angle, and left–right reversal of all images were fully corrected in all images. Moreover, patients’ positions were estimated with 96% accuracy. The mean correction time was approximately 0.4 s. Conclusion: The DL-based QA system quickly and accurately corrected CXR images.

[Background] In computed tomography (CT) for intracranial hemorrhage (ICH), the various window settings and the continuity of slices are critical factors for accurate diagnosis. However, traditional convolutional neural networks typically accept only single-slice images. Since ICH lesions often extend across multiple slices, using only single-slice images may result in reduced diagnostic accuracy by neglecting spatial continuity. Our approach addresses this limitation by integrating multi-slice information through a 9-channel pseudo-color map. To address this limitation, we explored the use of a 9-channel pseudo-color map for the discrimination of ICH in CT. [Method] A total of 21,744 cases (normal controls: 12,862; abnormal cases: 8882) from an open dataset were utilized for model training and validation. Abnormal cases included a variety of ICHs. The 9-channel pseudo-color map was generated by combining three different window settings with three continuous slices. ResNeXt50-32x4d architecture with five-fold cross-validation used. A total of 956 clinical cases were used for model testing. [Result] A total of 558,738 images were included in the model training process. The optimal model performance metrics were as follows: accuracy: 95.92%, sensitivity: 96.37%, and specificity: 95.24%. The average processing time for each case was recorded as 3.29 s. [Conclusions] The 9-channel pseudo-color map demonstrates high accuracy in the discrimination of ICH in CT images using deep learning methodologies.