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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies Displayed Sun B, Lam D, Yang D
A machine learning approach to the accurate prediction of monitor units for a compact proton machine.
Physical phantom measurements are commonly employed to determine field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. The goal of this study was to develop a secondary check tool for output factors (OF) measurements and eventually eliminate patient-specific OF measurements. The study concluded that machine learning methods can be used to predict OF for double-scatter proton machines with greater prediction accuracy than the most popular semi-empirical prediction model.
AHRQ-funded; HS022888.
Citation: Sun B, Lam D, Yang D .
A machine learning approach to the accurate prediction of monitor units for a compact proton machine.
Med Phys 2018 May;45(5):2243-51. doi: 10.1002/mp.12842..
Keywords: Imaging, Patient Safety, Tools & Toolkits