Quality Control System Research Articles

Mito-kaede photoactivation and chase experiment for mitophagy: optimizing flux measurement via fluid exchange system.

Modulating autophagy and mitophagy, vital cellular quality control systems, offer therapeutic potential for critical illnesses. However, limited drug screening options hinder progress. We present a novel assay using the photoswitchable fluorescent reporter, mito-Kaede, to quantify mitophagy flux. Mito-Kaede's superior UV-induced photoconversion and brightness post-conversion make it ideal for prolonged mitochondrial dynamics tracking. Its specificity in responding to mitophagy, confirmed by parkin-knockout cells, adds value. When coupled with a custom fluid exchange system, enabling efficient medium changes, precise mitophagy observations become feasible. This mitophagy assay, alongside our methodological insights, can decipher mitophagy's role in pathology and supports drug screening efforts.

Pharmacological inhibition of USP14 delays proteostasis-associated aging in a proteasome-dependent but foxo-independent manner

ABSTRACT Aging is often accompanied by a decline in proteostasis, manifested as an increased propensity for misfolded protein aggregates, which are prevented by protein quality control systems, such as the ubiquitin-proteasome system (UPS) and macroautophagy/autophagy. Although the role of the UPS and autophagy in slowing age-induced proteostasis decline has been elucidated, limited information is available on how these pathways can be activated in a collaborative manner to delay proteostasis-associated aging. Here, we show that activation of the UPS via the pharmacological inhibition of USP14 (ubiquitin specific peptidase 14) using IU1 improves proteostasis and autophagy decline caused by aging or proteostatic stress in Drosophila and human cells. Treatment with IU1 not only alleviated the aggregation of polyubiquitinated proteins in aging Drosophila flight muscles but also extended the fly lifespan with enhanced locomotive activity via simultaneous activation of the UPS and autophagy. Interestingly, the effect of this drug disappeared when proteasomal activity was inhibited, but was evident upon proteostasis disruption by foxo mutation. Overall, our findings shed light on potential strategies to efficiently ameliorate age-associated pathologies associated with perturbed proteostasis. Abbreviations: AAAs: amino acid analogs; foxo: forkhead box, sub-group O; IFMs: indirect flight muscles; UPS: ubiquitin-proteasome system; USP14: ubiquitin specific peptidase 14

Field Evaluation and Application of Intelligent Quality Control Systems

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction.

Protonic Ceramic Electrochemical Cell Manufacturing at Idaho National Laboratory

Protonic Ceramic Electrochemical Cells (PCECs), operating at intermediate temperatures of 400-600oC, show great potential for hydrogen production and versatile chemical manufacturing. As this technology rapidly evolves, scaling up production in both quantity and size is crucial. This step bridges the gap between fundamental academic research on small button cells (e.g., 10 mm to 1 inch) and higher-technology readiness level development of industry-standard units (> 10x10 cm2) for stack assembly. At Idaho National Laboratory, we've made significant strides in this transition by applying advanced manufacturing technologies and our expertise in materials science and engineering. Our approach integrates materials, structural, and interface engineering research and development into the cell fabrication process, guided by a stringent quality assurance and quality control (QA&QC) system. This ensures the reliable, repeatable, and durable performance of PCECs.

ASSESSMENT AND CURRENT TRENDS OF ENSURING FOOD SECURITY IN UKRAINE

The research on Ukraine's food security is of paramount importance, as it directly impacts the national agro-food systems. The article delves into the current state of internal food self-sufficiency in the Ukrainian population, conducting an analytical assessment of export-import transactions with agricultural raw materials and food products. The findings reveal a significant trend of increasing food exports in recent years, along with an evaluation of the export-import coverage for the main types of food products. Ukraine's place in the world rating of food security based on the Global Food Security Index was studied. The fundamental problems and reasons that determined Ukraine's decrease in the rating positions and the level of food security of its population were identified. In addition to the military conflict, Ukraine has a low economic availability of food products for the population and a low level of sustainability in the domestic agro-food system. Despite the war's consequences, the agricultural business remains one of the most profitable economic activities in Ukraine. This analysis of individual indicators of the effectiveness of the country's agrarian sector as a basis for ensuring food security underscores the sector's potential. The modern production structure of the main types of agricultural products by categories of producers was studied. The key trends accompanying ensuring food security in Ukraine under modern conditions are summarized. It was revealed that food security in the crop sector is achieved at the expense of the public sector agrarian formations, mainly at the expense of households in the livestock sector. Based on the average values ​​of the actual consumption indicators, the state of providing the population with the main types of food was analyzed. The results showed problematic aspects in providing the country's population with meat and dairy products, fish, fruits, berries, and grapes. The priority directions for ensuring and strengthening food security on a national scale are substantiated. Among the main ones, the following are highlighted: the need to implement sustainable farming and animal husbandry practices, further improvement of the food quality management and control system, protection of biodiversity of ecological agrosystems, optimization and reduction of value chains, increasing the level of social responsibility of food producers and consumers.

Comparative proteomic discovery of salt stress response in alfalfa roots and overexpression of MsANN2 confers salt tolerance

Soil salinity constrains growth, development and yield of alfalfa (Medicago sativa L.). To illustrate the molecular mechanisms responsible for salt tolerance, a comparative proteome analysis was explored to characterize protein profiles of alfalfa seedling roots exposed to 100 and 200 mM NaCl for three weeks. There were 52 differentially expressed proteins identified, among which the mRNA expressions of 12 were verified by Real-Time-PCR analysis. The results showed increase in abundance of ascorbate peroxidase, POD, CBS protein and PR-10 in salt-stressed alfalfa, suggesting an effectively antioxidant and defense systems. Alfalfa enhanced protein quality control system to refold or degrade abnormal proteins induced by salt stress through upregulation of unfolded protein response (UPR) marker PDIs and molecular chaperones (eg. HSP70, TCP-1, and GroES) as well as the ubiquitin‐proteasome system (UPS) including ubiquitin ligase enzyme (E3) and proteasome subunits. Upregulation of proteins responsible for calcium signal transduction including calmodulin and annexin helped alfalfa adapt to salt stress. Specifically, annexin (MsANN2), a key Ca2+-binding protein, was selected for further characterization. The heterologous of the MsANN2 in Arabidopsis conferred salt tolerance. These results provide detailed information for salt-responsive root proteins and highlight the importance of MsANN2 in adapting to salt stress in alfalfa.

A Quality Control System for Historical In Situ Precipitation Data

ABSTRACT In this study, a comprehensive quality control (QC) system for in-situ precipitation data records was developed and applied to Canadian in situ precipitation datasets. The system includes a pair of screening procedures to screen for two types of random errors: one procedure is applied to the untransformed monthly total precipitation data series, which is good at finding erroneous data of unusually large values; another is applied to the log-transformed monthly precipitation data (in mm) series, log(P + 0.1), which is good at identifying erroneous zero or near-zero monthly total precipitation amounts. The system then applies three QC (threshold, kriging, and temporal) tests and a decision-making process to confirm whether the screened suspects are erroneous. There is generally good agreement between all the QC tests, while the decision-making process yields the most accurate results when compared to the manually reviewed results. The QC work on Canadian precipitation data sets revealed that it is necessary to apply a pair of screening procedures to identify both types of random errors. All the monthly values identified to be erroneous are set to missing, and so are the corresponding daily values, while keeping records of the original data.

The Application of Analytic Hierarchy Process Method in the Evaluation of Educational Quality in Colleges and Universities

Talent quality is the lifeline of colleges and universities. Based on employment guidance, this paper establishes a student quality evaluation and control system including knowledge, moral character, and ability, and uses the Analytic Hierarchy Process (AHP) method to analyze the factors affecting the quality of education in colleges and universities. In addition, some suggestions are provided to improve the quality of students.

The HtrA chaperone monitors sortase-assembled pilus biogenesis in Enterococcus faecalis.

Sortase-assembled pili contribute to virulence in many Gram-positive bacteria. In Enterococcus faecalis, the endocarditis and biofilm-associated pilus (Ebp) is polymerized on the membrane by sortase C (SrtC) and attached to the cell wall by sortase A (SrtA). In the absence of SrtA, polymerized pili remain anchored to the membrane (i.e. off-pathway). Here we show that the high temperature requirement A (HtrA) bifunctional chaperone/protease of E. faecalis is a quality control system that clears aberrant off-pathway pili from the cell membrane. In the absence of HtrA and SrtA, accumulation of membrane-bound pili leads to cell envelope stress and partially induces the regulon of the ceftriaxone resistance-associated CroRS two-component system, which in turn causes hyper-piliation and cell morphology alterations. Inactivation of croR in the OG1RF ΔsrtAΔhtrA background partially restores the observed defects of the ΔsrtAΔhtrA strain, supporting a role for CroRS in the response to membrane perturbations. Moreover, absence of SrtA and HtrA decreases basal resistance of E. faecalis against cephalosporins and daptomycin. The link between HtrA, pilus biogenesis and the CroRS two-component system provides new insights into the E. faecalis response to endogenous membrane perturbations.

Deep Quality Cost Report Analysis Defective Product Control

The importance of quality costs for cigarette companies is to find out the extent to which the quality control system implemented by the company functions, if the lower the quality costs indicate the better the products produced by the cigarette company. The research uses a qualitative descriptive analysis method. Descriptive analysis was used to describe the variables in the research, while qualitative analysis was carried out to produce findings in the form of descriptions of research subjects and objects based on facts in field data obtained in research at the Maesan Bondowoso Black Crow Cigarette Company. based on research results, the Black Crow Cigarette Company, located in Pakuniran Village, Maesan District, Bondowoso Regency, East Java, was founded in 2004 by Mr. Nawar H. Wasıl. Basically, the company has implemented costs related to product quality, but the company has not specifically identified and categorized them. Therefore, the company should pay attention to costs that constitute quality and group these costs based on their classification.

Use of wireless technologies in IoT projects

When it comes to creating projects based on the use of the Internet of Things (IoT), wireless sensor networks are often used. Edge computing in IoT technology reduces system response delays to sensor output signals and increases the network throughput. At the same time, a short-range sensor network can work locally without access to the Internet, while long-range networks, as a rule, require access to the Internet and use both edge and cloud computing. The paper shows the features of the application of both peripheral and cloud computing in the practical implementation of IoT projects with various methods of wireless data transmission. Specific examples show the possibility of data transmission over a short distance using ESP-NOW technology, nRF24L01 radio modules and creating a local Wi-Fi access point. The range of sensor data transmission between microcontrollers is practically determined for each proposed option. The calculation of the range of the LoRa radio line is carried out for the actual sensitivity values of the receiver. The Okamura-Hata radio wave propagation model is proposed to estimate the LoRa radio line’s total loss. The operational range of LoRa modules with different types of signal modulation is practically determined. The essence of edge computing combined with digital and analogue sensors is shown. Peculiarities of peripheral computing during the implementation of IoT projects for remote control of blood saturation of patients using wireless technologies are considered. An example of data transmission of the air quality control system through a gateway based on an ESP8266 microcontroller with a graphical display of measurements in the IoT cloud service ThingSpeak and Blynk was provided.

DeepQC: A deep learning system for automatic quality control of in-situ soil moisture sensor time series data

Amidst changing climate, real-time soil moisture monitoring is vital for the development of in-season decision support tools to help farmers manage weather-related risks in agriculture. Precision Sustainable Agriculture (PSA) recently established a real-time soil moisture monitoring network across the central, Midwest, and eastern U.S., but continuous field-scale sensor observations often come with data gaps and anomalies. To maintain the data quality and continuity needed for developing decision tools, a quality control system is necessary.The International Soil Moisture Network (ISMN) introduced the Flagit module for anomaly detection in soil moisture time series observations. However, under certain conditions, Flagit's threshold and spectral based quality control approaches may underperform in identifying anomalies. Recently, deep learning methods have been successfully applied to detect time series anomalies in time series data in various disciplines. However, their use in agriculture for anomaly detection in time series datasets has not been yet investigated. This study focuses on developing a Bi-directional Long Short-Term Memory (LSTM) model, referred to as DeepQC, to identify anomalies in soil moisture time series data. Manual flagged PSA observations were used for training, validation, and testing the model, following an 80:10:10 split. The study then compared the DeepQC and Flagit-based estimates to assess their relative performance.Flagit correctly flagged 95.8 % of the correct observations and 50.3 % of the anomaly observations, indicating its limitations in identifying anomalies, particularly at sites consists of more than 30 % anomalies. On the other hand, the DeepQC correctly flagged 89.8 % of the correct observations and 99.5 % of the anomalies, with overall accuracy of 95.6 %, in significantly less time, demonstrating its superiority over Flagit approach. Importantly, the performance of the DeepQC remained consistent regardless of the number of anomalies in site observations. Given the promising results obtained with the DeepQC, future studies will focus on implementing and finetuning this model on national and global soil moisture networks.

The Role of Protein Quantity Control in Polyglutamine Spinocerebellar Ataxias.

Polyglutamine spinocerebellar ataxias (polyQ SCAs) represent the most prevalent subtype of SCAs. The primary pathogenic mechanism is believed to be the gain-of-function neurotoxicity of polyQ proteins. Strategies such as enhancing the degradation or inhibiting the accumulation of these mutant proteins are pivotal for reducing their toxicity and slowing disease progression. The protein quality control (PQC) system, comprising primarily molecular chaperones and the ubiquitin‒proteasome system (UPS), is essential for maintaining protein homeostasis by regulating protein folding, trafficking, and degradation. Notably, polyQ proteins can disrupt the PQC system by sequestering its critical components and impairing its proteasomal functions. Therefore, restoring the PQC system through genetic or pharmacological interventions could potentially offer beneficial effects and alleviate the symptoms of the disease. Here, we will provide a review on the distribution, expression, and genetic or pharmacological intervention of protein quality control system in cellular or animal models of PolyQ SCAs.

Kyrgyz Republic production-resource and export potential state regulation mechanism

The study addressed the mechanism of state regulation of the production, resource and export potential of the Kyrgyz Republic. The study aimed to improve the system of state regulation of the agricultural sector to increase national food security. The analysis revealed a steady increase in production of the main types of agricultural products over the period 2014-2024: cereals by 11.6%, potatoes by 12.5%, vegetables by 18.2%, meat by 24.5% and milk by 17.1%. However, the study also showed uneven levels of self-sufficiency across different types of products: the high levels of self-sufficiency in potatoes (167.4%), vegetables (164.5%) and milk (116.5%) contrast with the low levels of self-sufficiency in fruit and berries (18.2%), vegetable oil (44%) and sugar (69%). An analysis of the existing state regulatory mechanisms revealed a comprehensive legal framework, including the Law of the Kyrgyz Republic No. 320 “On Food Security” and the “Food Security and Nutrition Programme for 2019-2023”, but their implementation is limited by insufficient funding and administrative barriers. Based on the analysis, recommendations were developed to optimise the state regulatory mechanism, including the introduction of a differentiated subsidy system, optimisation of the structure of exports and imports of agricultural products, increasing the efficiency of agricultural production, development of agricultural processing and improving the food quality control system. Proposed measures include the creation of the brand “Ecologically clean products of Kyrgyzstan”, the introduction of the Global G.A.P. certification system, the creation of a network of regional agricultural-consulting centres, the development of agro-industrial parks and the modernisation of quality control laboratories. The implementation of these measures aims to overcome the identified problems create more favourable conditions for the development of agriculture and ensure food security in the Kyrgyz Republic

Deep learning-based prediction of 3-dimensional silver contact shapes enabling improved quality control in solar cell metallization

The industrial metallization of Si solar cells predominantly relies on screen printing, with silver as the preferred electrode material. However, the design of commercial screens often leads to suboptimal silver usage and increased electrical resistance due to print-related inhomogeneities like mesh marks, constrictions and spreading. Real-time monitoring of quality parameters during production has thus become increasingly critical. Current inline optical quality control systems usually only include 2D visualizations of the printed layout, which limits their effectiveness in quality control. Options that allow 3D measurements are usually slow, expensive, and therefore not worth considering in most cases. This research focuses on the development of a model that can estimate the three-dimensional shape of printed contact fingers from a single 2D image without the need of additional hardware using deep learning. Furthermore, a workflow for the generation of training data, which involves the creation of image pairs from a 2D microscope and a 3D confocal laser scanning microscope (CLSM) to accurately represent solar cell fingers, is presented. After model training, the predicted height maps are compared with the ground truth height maps, and the robustness of the model with respect to a paste variation and screen parameter variation is examined. The results confirm the feasibility and reliability of deep learning-based 3D shape estimation, extending its applicability to new, previously unseen data from screen-printed contact fingers. With a structural similarity index (SSIM) score of 0.76, a strong correlation between the estimated and ground truth height maps is established. In summary, our deep learning-based approach for height map estimation offers an effective and reliable solution for fast inline detection and analysis of the cross-sectional area of the printed contact fingers.

Association between anesthesia assistance and precancerous lesions and early cancer detection during diagnostic esophagogastroduodenoscopy: a propensity score-matched retrospective study.

Esophagogastroduodenoscopy (EGD) is a fundamental procedure for early detection of upper gastrointestinal (UGI) cancer. However, limited research has been conducted on the impact of sedation during EGD on the identification of precancerous lesions and early cancer (EC). This retrospective study aims to evaluate whether sedation during EGD can improve the detection rates of precancerous lesions and EC. In this propensity score-matched retrospective study, we examined medical records from outpatients who underwent diagnostic EGD at a large tertiary center between January 2023 and December 2023. Data on endoscopic findings and histology biopsies were obtained from an endoscopy quality-control system. The primary objective was to compare the rates of detecting precancerous lesions and EC in patients who received sedation during EGD vs. those who did not receive sedation. Additionally, we aimed to identify factors influencing these detection rates using binary logistic regression analysis. Following propensity score matching, a total of 17,862 patients who underwent diagnostic EGD with or without propofol sedation were identified. The group that received sedation exhibited a higher detection rate of precancerous lesions and EC in comparison to the non-sedated group (1.04 vs. 0.75%; p = 0.039). Additionally, within the sedated group, there was an increased likelihood of identifying precancerous lesions and EC specifically at the gastric antrum (0.60 vs. 0.32%, p = 0.006). Binary logistic regression analysis demonstrated that independent risk factors influencing the detection rates included age, gender, observation time, and number of biopsies conducted during the procedure. Anesthesia assistance during EGD screening proved advantageous in detecting EC as well as precancerous lesions. It is crucial for endoscopists to consider these factors when performing EGD screening procedures.

FetMRQC: A robust quality control system for multi-centric fetal brain MRI

Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts’ ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC’s predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain.

Construction and application of an ICU nursing electronic medical record quality control system in a Chinese tertiary hospital: a prospective controlled trial

BackgroundICU nurses provide critical care and meticulously document electronic medical records (EMRs), tracking vital signs, interventions, and medication hourly. Despite China’s ICUs effectively integrating real-time monitor and ventilator data into EMRs, challenges persist. Patient movements can introduce inaccuracies, and the demands of critical care may lead nurses to miss assessments like pain and nutrition. Traditional manual EMR verification is inefficient and error-prone, highlighting the urgent need for standardized, technology-aided EMR practices in ICU nursing.ObjectiveThis study aimed to describe the development and evaluation of an electronic medical records quality control system implemented in a Chinese tertiary care ICU setting, where current practices impact the accuracy of electronic medical records.MethodsA prospective controlled trial was conducted with 600 ICU patients in Zhejiang Province from January to December 2023. An automated EMR quality control system was implemented in July 2023, facilitating real-time data collection and quality control for vital signs, medication management, and nursing evaluations.ResultsAfter implementing the ICU nursing electronic medical record quality control system, the prevalence of false data on vital signs decreased from 9 to 1.33%. Additionally, the incidence of incomplete medication administration dropped from 3.33 to 1.67%, and the rate of missing evaluations of assessment items in EMRs was reduced from 8 to 1.33%. Besides, the average time spent on quality control of the electronic medical records was 62 (48,76) seconds per record, which was significantly lower than the 264 (195.5,337.5) seconds using the traditional method. The nurses’ satisfaction with the nursing electronic medical record quality control was (105.73 ± 9.31).ConclusionsThe ICU nursing electronic medical record quality control system has led to substantial improvements in the quality and reliability of EMRs. The reduction in false data on vital signs, instances of incomplete medication administration, and missing evaluations of assessment items demonstrates the system’s positive impact on nursing documentation practices. These improvements not only enhance the accuracy of patient records but also contribute to better patient care and safety within the ICU setting.

Risk assessment in production of functional food products by means of RCA method

According to the results of review of the regulatory legal acts and standardization documents, the necessity has been determined to develop a methodology for risk assessment in production of functional food products and its introduction into operation for implementation of the quality control system of food products fortified with iodine. This article represents the description of development of risk assessment methodology assigned for assessing the quality control system for functional foods fortified with iodine. The hazardous event in this case is the non-compliance of the food product with the regulatory requirements, in particular the decrease of iodine content in the functional foods down to a level below 30 mcg/100g of the product, which is less than 15% of an adult person's daily requirement. To assess the probable risks that can affect the peculiar features of the functional foods, RCA root cause mapping system was proposed that includes the standard methods: Ishikawa cause and effect diagram, fault tree analysis FTA, failure mode and effect analysis FMEA, and Pareto analysis. The technology of producing of the cooked sausages fortified with iodine-containing food ingredients was chosen as the object of research, as this technology is associated with a wide variety of technological modes and methods of raw materials processing, as well as the storage conditions of the ready products. The results of the study included a review of the collected data, consideration of the hypothesis, expert opinion on the most probable root causes of failures and losses, and the recommended preventive and corrective actions.

Automatic quality assessment of knee radiographs using knowledge graphs and convolutional neural networks.

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. 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. 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. 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. 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.