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Background: X‐ray radiography is a widely used imaging technique worldwide, and its image quality directly affects diagnostic accuracy. Therefore, X‐ray image quality control (QC) is essential. However, subjectively assessing image quality is inefficient and inconsistent, especially when large amounts of image data are being evaluated. Thus, subjective assessment cannot meet current QC needs. Purpose: To meet current QC needs and improve the efficiency of image quality assessment, a complete set of quality assessment criteria must be established and implemented using artificial intelligence (AI) technology. Therefore, we proposed a multi‐criteria AI system for automatically assessing the image quality of knee radiographs. Methods: A knee radiograph QC knowledge graph containing 16 "acquisition technique" labels representing 16 image quality defects and five "clarity" labels representing five grades of clarity were developed. Ten radiographic technologists conducted three rounds of QC based on this graph. The single‐person QC results were denoted as QC1 and QC2, and the multi‐person QC results were denoted as QC3. Each technologist labeled each image only once. The ResNet model structure was then used to simultaneously perform classification (detection of image quality defects) and regression (output of a clarity score) tasks to construct an image QC system. The QC3 results, comprising 4324 anteroposterior and lateral knee radiographs, were used for model training (70% of the images), validation (10%), and testing (20%). The 865 test set data were used to evaluate the effectiveness of the AI model, and an AI QC result, QC4, was automatically generated by the model after training. Finally, using a double‐blind method, the senior QC expert reviewed the final QC results of the test set with reference to the results QC3 and QC4 and used them as a reference standard to evaluate the performance of the model. The precision and mean absolute error (MAE) were used to evaluate the quality of all the labels in relation to the reference standard. Results: For the 16 "acquisition technique" features, QC4 exhibited the highest weighted average precision (98.42% ± 0.81%), followed by QC3 (91.39% ± 1.35%), QC2 (87.84% ± 1.68%), and QC1 (87.35% ± 1.71%). For the image clarity features, the MAEs between QC1, QC2, QC3, and QC4 and the reference standard were 0.508 ± 0.021, 0.475 ± 0.019, 0.237 ± 0.016, and 0.303 ± 0.018, respectively. Conclusions: The experimental results show that our automated quality assessment system performed well in classifying the acquisition technique used for knee radiographs. The image clarity quality evaluation accuracy of the model must be further improved but is generally close to that of radiographic technologists. Intelligent QC methods using knowledge graphs and convolutional neural networks have the potential for clinical applications. [ABSTRACT FROM AUTHOR]

Chest X-ray report generation aims to generate expert-level radiology diagnostic reports that can be used in clinics on given chest X-ray images. Automatic chest X-ray report generation can assist doctors in writing radiology reports, which can not only improve the diagnostic efficiency of radiologists and meet the needs of a large number of patients but also remind some inexperienced radiologists of misdiagnoses or missed diagnoses. As a visual-language task, chest X-ray report generation can be achieved by existing image captioning methods. However, chest X-ray imagery exhibits a high degree of similarity, wherein diagnostically crucial abnormalities often manifest in minute regions of the images, posing a formidable challenge in discerning subtle visual differences and distinctive features. Further, the medical findings have been widely applied as labels to guide the image encoding module to focus on the salient regions in the image using the attention mechanism. However, existing methods neglect the relations between class features corresponding to different medical findings and the relationship between such local features and global features of the image. To tackle these challenges, we propose dual graph convolution networks (DGCN) that novelly incorporate the grid feature graph and class feature graph into the report generation process. In detail, the grid graph is used to explore the relationship between the grid regions on the whole image, and the class feature graph is used to mine the relationship between different medical findings (Dual GCN). Moreover, to explore the relationship between global features, such as grid features, and local features, such as class features, we introduce a multi-head attention module to help fuse different types of features and mine more useful relationship information (FFARM). Extensive experiments on the IU X-ray dataset underscore the substantial superiority of our model over the state-of-the-art chest X-ray report generation methodologies. Further ablation analyses reveal that both the class feature graph and the grid feature graph contribute significantly to enhancing the efficacy of chest X-ray report generation. [ABSTRACT FROM AUTHOR]

Neurological disorders (NDs) are diseases that seriously affect the health of individuals worldwide, potentially leading to a significant reduction in the quality of life for patients and their families. Herbal medicines have been widely used in the treatment of NDs due to their multi-target and multi-pathway features. Ginkgo biloba leaves (GBLs), one of the most popular herbal medicines in the world, have been demonstrated to present therapeutic effects on NDs. However, the pharmacological mechanisms of GBLs in the treatment of neurological disorders have not been systematically summarized. This study aimed to summarize the molecular mechanism of GBLs in treating NDs from the cell models, animal models, and clinical trials of studies. Four databases, i.e., PubMed, Google Scholar, CNKI, and Web of Science were searched using the following keywords: "Ginkgo biloba", "Ginkgo biloba extract", "Ginkgo biloba leaves", "Ginkgo biloba leaves extract", "Neurological disorders", "Neurological diseases", and "Neurodegenerative diseases". All items meeting the inclusion criteria on the treatment of NDs with GBLs were extracted and summarized. Additionally, PRISMA 2020 was performed to independently evaluate the screening methods. Out of 1385 records in the database, 52 were screened in relation to the function of GBLs in the treatment of NDs; of these 52 records, 39 were preclinical trials and 13 were clinical studies. Analysis of pharmacological studies revealed that GBLs can improve memory, cognition, behavior, and psychopathology of NDs and that the most frequently associated GBLs are depression, followed by Alzheimer's disease, stroke, Huntington's disease, and Parkinson's disease. Additionally, the clinical studies of depression, AD, and stroke are the most common, and most of the remaining ND data are available from in vitro or in vivo animal studies. Moreover, the possible mechanisms of GBLs in treating NDs are mainly through free radical scavenging, anti-oxidant activity, anti-inflammatory response, mitochondrial protection, neurotransmitter regulation, and antagonism of PAF. This is the first paper to systematically and comprehensively investigate the pharmacological effects and neuroprotective mechanisms of GBLs in the treatment of NDs thus far. All findings contribute to a better understanding of the efficacy and complexity of GBLs in treating NDs, which is of great significance for the further clinical application of this herbal medicine. [ABSTRACT FROM AUTHOR]