Machine Learning Techniques Reveal the Most Effective and Influencing Gender-based Scan Acquisition Parameters that Contribute to Patient Dose Undergoing Enhanced CT abdomen and pelvis examinations

Using machine learning (ML) techniques to determine factors affecting radiation dose for patients undergoing enhanced abdomen and pelvis computed tomography (CT). Factors such as patient characteristics, CT scan acquisition parameters, and patient misalignment were considered as input variables. CT dose indices namely dose-length product (DLP), volumetric CT dose index (CTDI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vol</inf>), size-specific dose estimate (SSDE), size-specific estimate based on water equivalent diameter (SSDEWED), and the effective dose (effD) were considered output variables. Herein, 5 different ML algorithms were used, the Logistic Regression (LR), Random forests (RF), Neural Network (NN), k-Nearest Neighbors Algorithms (kNN), and Support Vector Machine (SVM). Demonstration of the most accurate ML technique to predict the most effective and influencing input variables will be included. Own to our data classification setup we were able to determine the most effective gender-based input variables. Although input variables for males and females were the same, their influence and ranking were different.