Enhancing quality control in radiography systems through automated algorithms

Quality control (QC) in radiography systems is essential for ensuring imaging accuracy and radiation safety. Traditional QC methods are time-consuming and prone to human error. Automated algorithms offer a potential solution by streamlining QC assessments. The aim of this study was to develop and evaluate the performance of an automated QC algorithm tailored for radiography systems in accordance with Malaysian regulatory requirements by comparing its efficiency, accuracy, and reliability against manual methods.​ QC algorithms were developed using Microsoft Excel for a comprehensive list of QC tests delineated by the Ministry of Health, Malaysia. These algorithms were designed to automate the input of measured data, execute necessary calculations, and deliver a pass/fail outcome based on established regulatory criteria. The performance of the algorithms was evaluated through data collected from 100 radiography machines across various locations and models. The study compared the time efficiency, accuracy and errors of algorithm-based QC assessments and manual calculations. The algorithm significantly (p < 0.001) reduced QC assessments time by 90% and eliminated all calculation errors, outperforming manual assessments that occurred 64 times. This is the first Excel-based QC algorithm validated against Malaysian regulations, providing low-cost alternative to commercial systems.