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Radiation Dose Optimisation Through Artificial Intelligence (AI)-based Auto-Thorax Collimation.

Derek Yoon-Sang Lee¹, Nicole Chan¹, Richard Knight¹

¹Monash Imaging, Monash Health, Casey Hospital, Berwick, Victoria, Australia.

Journal of Medical Radiation Sciences

|March 27, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) in radiography improves auto-thorax collimation (ATC) consistency over manual methods. While results are similar, AI enhances standardization and reproducibility for efficient, safe chest X-ray imaging.

Keywords:

artificial intelligence auto‐thorax collimation collimation dose optimisation radiation dose radiography

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Artificial intelligence (AI) is increasingly adopted in digital radiography.
Auto-thorax collimation (ATC) automates field size selection in chest X-ray (CXR) imaging.
Clinical performance of ATC requires further evaluation.

Purpose of the Study:

Evaluate the clinical performance and collimation consistency of AI-based auto-thorax collimation (ATC) compared to manual collimation.
Assess the impact of ATC on workflow efficiency and radiation safety in routine CXR practice.

Main Methods:

Retrospective analysis of 400 posteroanterior (PA) erect CXRs.
Measurement of collimation size in superior-inferior and lateral dimensions.
Assessment of repeat rates due to collimation errors in 200 additional CXRs.
Statistical analysis using t-tests and Z-tests; calculation of coefficient of variation (CV) for collimation variability.

Main Results:

ATC showed tighter inferior and left lateral collimation than manual methods (p < 0.05).
Manual collimation demonstrated tighter superior collimation.
AI-based ATC exhibited greater collimation consistency with lower CV values across operators.
Repeat rates for ATC (7%) and manual collimation (8%) were comparable.

Conclusions:

AI-based collimation offers similar results to manual methods but with improved standardization and reproducibility.
Consistent output from AI-based collimation suggests benefits in high-turnover environments.
AI in radiography enhances workflow efficiency and optimizes radiation safety.