ArticleJournal of applied clinical medical physics2026
Machine learning for optimizing mAs in KUB radiography with metal implants.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine learning for optimizing mAs in KUB radiography with metal implants.Journal of applied clinical medical physics · 2026Article
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5 authors.
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Abstract
background and purposeKidney-ureter-bladder (KUB) radiography is a common examination that exposes patients to a higher radiation dose and increased cancer risk; therefore, it is important to estimate suitable exposure factors for each patient prior to radiography. The present study aimed to utilize machine learning (ML) approach to predicting the suitable milliampere-seconds (mAs) and reducing overexposure in patients with metal implants during KUB radiography.
methodsA phantom was used to understand the effect of metal implants on radiation exposure during KUB radiography with automatic exposure control (AEC) technique. Subsequently, we retrospectively enrolled 619 subjects, including 56 with metal implants and 563 without, from one hospital (group A) and 323 subjects, including 89 with metal implants and 234 without, from another hospital (group B). All subjects underwent both KUB radiography and physiological examinations on the same day. Data on body parameters and exposure factors were retrieved from hospital database. To train the prediction model, the dataset of group A without metal implants was randomly divided into 80% and 20% for training and testing sets, respectively. Five different ML algorithms were utilized to train the prediction model using 10-fold cross-validation. The correlation coefficients (CC), mean average error (MAE), normalized root mean squared errors (nRMSE), and R-square (R
resultsThe phantom experiment demonstrated that the metal plate significantly increased the mAs and reached exposure (REX) values when using AEC technique during KUB radiography. The comparison of patient data showed that the patients with metal implants had significantly higher mAs and REX than those without in both groups. In group A, the ML comparisons showed that the artificial neural network (ANN) model outperformed other ML models in predicting mAs based on the testing set, exhibiting the highest CC of 0.791 ± 0.007 and R
conclusionWe concluded that the ML approach is suitable for building the model for predicting appropriate mAs and reducing overexposure in patients with metal implants during KUB radiography.
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