Ensuring the quality of chest radiographs is essential for accurate diagnostics and effective training of artificial intelligence models in radiology. Traditional quality control (QC) methods require significant manual effort from radiologists, limiting efficiency and scalability. To address these challenges, a comprehensive automated QC pipeline has been developed, integrating both statistical and AI-driven tools. The pipeline, AutoQC, enables large-scale data curation, standardisation and refinement to improve radiograph quality while mitigating biases that could compromise AI model generalisation.
Quality control is a crucial step in medical imaging, ensuring that the images used for diagnostics and AI training are reliable and free from systematic errors. Variations in imaging equipment, patient positioning and external annotations can introduce biases that affect both human interpretation and AI model predictions. Addressing these inconsistencies through automation allows for a more uniform and unbiased dataset, improving the overall quality of radiological assessments and AI performance.
Automated Quality Control Pipeline
AutoQC is structured into multiple stages to systematically refine radiographs and prepare them for further use. The process begins with pixel intensity transformations to standardise image contrast across different manufacturers. Differences in imaging equipment can introduce discrepancies in brightness and contrast, which may mislead AI models and radiologists. By applying transformation techniques, the pipeline ensures a consistent pixel intensity distribution across images, reducing manufacturer-dependent variations.
Grayscale inversion correction is the next step, ensuring a uniform orientation of radiographs. Some imaging systems store images with inverted grayscale values, which can confuse automated analysis and radiologists. AutoQC detects and corrects these inversions, ensuring that all radiographs follow the same intensity conventions, thus improving interpretability and AI training consistency.
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The system then applies text and annotation removal to eliminate potential shortcut opportunities for AI models. Many radiographs contain labels, markers or other textual information that AI models may learn as predictive cues rather than focusing on the actual pathology. AutoQC employs sophisticated inpainting techniques to remove these elements without damaging the underlying anatomical structures. This reduces the likelihood of spurious correlations and enhances model generalisability.
Additionally, cropping and aspect ratio analysis help to refine image framing, eliminating excessive padding and non-relevant content. Many radiographs include regions of unnecessary information, such as black borders or incorrectly framed scans. AutoQC detects and removes these extraneous areas while maintaining the integrity of the lung fields. Ensuring a consistent aspect ratio across radiographs is crucial, as varying proportions may influence AI models and introduce unwanted biases.
These steps enhance the consistency of chest radiographs while preserving their diagnostic integrity. By structuring the QC process in a systematic manner, AutoQC provides a robust framework for ensuring high-quality, standardised radiological data.
Performance Evaluation and Results
To validate the effectiveness of AutoQC, multiple datasets from different countries and imaging equipment manufacturers were used. These datasets included radiographs acquired from various clinical settings, ensuring a diverse test environment. The system demonstrated high sensitivity and specificity in identifying inconsistencies such as lateral projection errors, incorrect aspect ratios and misoriented images. By leveraging deep learning models trained on well-annotated datasets, AutoQC reliably distinguished between correctly and incorrectly processed images, improving overall data reliability.
Image quality scoring, based on metrics like entropy and sharpness, successfully flagged radiographs with suboptimal contrast or excessive noise. These scoring mechanisms were designed to identify images with poor exposure, excessive blurring or other quality issues that could affect diagnostic accuracy. AutoQC’s ability to automatically detect and flag such radiographs ensures that only high-quality images are included in AI training and clinical workflows.
The pipeline also proved effective in identifying images with pacemakers or other high-intensity objects that could confound AI-based diagnostic models. High-density artefacts such as pacemakers, metal implants and medical devices can distort AI model predictions if not properly accounted for. AutoQC detects and annotates such features, allowing for appropriate handling during AI training and preventing misclassification.
These evaluations confirmed the robustness of AutoQC in ensuring standardised, high-quality radiographic data. By applying rigorous statistical validation techniques, the pipeline demonstrates its reliability in diverse clinical environments, making it a valuable tool for radiology departments and AI researchers alike.
Despite its strong performance, AutoQC has certain limitations. Deep learning models may still be susceptible to confounding factors, particularly in datasets with inherent biases that cannot be entirely removed through preprocessing. The system was developed using radiographs collected during the COVID-19 pandemic, which may affect its applicability to broader patient populations. Moreover, some metadata inconsistencies in DICOM files present challenges in standardisation, requiring additional validation in certain cases. To improve generalisability, further testing on additional external datasets is recommended, with refinements made as necessary to maintain high accuracy across different imaging conditions.
Implications for AI and Radiology
By automating QC, AutoQC significantly reduces the burden on radiologists, allowing them to focus on complex diagnostic tasks rather than routine data cleaning. Manual QC processes are time-consuming and prone to subjectivity, as different radiologists may apply different criteria when assessing image quality. Automation ensures that all images are evaluated using consistent, reproducible criteria, enhancing the reliability of both human and AI-driven interpretations.
This approach improves AI model performance by minimising biases introduced through annotation artefacts, equipment variations or inconsistent imaging practices. AI models trained on high-quality, standardised datasets perform more accurately and generalise better across diverse populations. This is particularly important for clinical applications, where incorrect predictions could have serious consequences for patient care.
Moreover, the system offers interactive reporting features, enabling radiologists and data scientists to review and adjust QC parameters as needed. AutoQC provides detailed statistical summaries and visual reports that facilitate data analysis and decision-making. By allowing users to fine-tune QC settings based on their specific requirements, the system ensures flexibility while maintaining standardisation.
The integration of AutoQC into radiology workflows fosters greater reproducibility, fairness and reliability in AI-driven medical imaging applications. By addressing key sources of variability in chest radiographs, the pipeline enhances trust in AI models and supports the adoption of automated QC methods in clinical practice.
AutoQC significantly enhances radiograph quality control by using automation to standardise data and reduce bias. By curating high-quality, unbiased datasets for AI model training, it improves diagnostic capabilities and supports reliable AI-assisted diagnostics, leading to fewer errors and better patient outcomes. Future research should explore its use in other imaging modalities like CT scans, MRIs and ultrasounds. The adoption of tools like AutoQC is crucial for achieving reliable and equitable AI-assisted radiology, as ensuring the quality of training data is vital for improving patient care and clinical acceptance.
Source: Radiology: Artificial Intelligence
Image Credit: iStock
