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Machine learning for optimizing mAs in KUB radiography with metal implants.

Wen-Xuan Chen1, Jen-Pei Su2, Shih-Hua Huang3

  • 1Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.

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|January 30, 2026
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Summary
This summary is machine-generated.

Machine learning accurately predicts radiation exposure (mAs) for kidney-ureter-bladder radiography in patients with metal implants. This approach reduces overexposure and associated cancer risks.

Keywords:
KUB radiographymAsmachine learningtarget exposure indicator

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Area of Science:

  • Radiological Physics
  • Medical Imaging
  • Machine Learning in Healthcare

Background:

  • Kidney-ureter-bladder (KUB) radiography is a common diagnostic tool.
  • Patients with metal implants pose challenges due to increased radiation dose and cancer risk.
  • Optimizing exposure factors is crucial for patient safety.

Purpose of the Study:

  • To develop a machine learning (ML) model for predicting optimal milliampere-seconds (mAs) in KUB radiography.
  • To reduce radiation overexposure in patients with metal implants.
  • To enhance radiation dose management in medical imaging.

Main Methods:

  • Utilized a phantom study to assess metal implant effects on radiation exposure with automatic exposure control (AEC).
  • Retrospectively analyzed data from 942 patients (145 with metal implants) across two hospitals.
  • Trained and validated five ML algorithms, including artificial neural network (ANN), using 10-fold cross-validation and transfer learning.

Main Results:

  • Phantom experiments confirmed metal implants increase mAs and reached exposure (REX) values.
  • Patients with metal implants showed significantly higher mAs and REX compared to those without.
  • The ANN model demonstrated superior performance, achieving high correlation coefficients (CC) and R-squared (R²) values in both internal and external validation sets.
  • ANN-predicted mAs for patients with metal implants were significantly lower than AEC-derived values.

Conclusions:

  • Machine learning is a viable approach for predicting suitable mAs in KUB radiography.
  • The developed ML model effectively reduces overexposure in patients with metal implants.
  • This study highlights the potential of ML for optimizing radiation dose in diagnostic imaging.