TY - JOUR 
A1 - García Sánchez, Antonio Javier
AU - García Angosto, Enrique Ángel
AU - Llor Álvarez, José Luis
AU - Serna Berna, Alfredo
AU - Ramos Amores, David
T1 - Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests
Y1 - 2019
SN - 1424-8220
UR - http://hdl.handle.net/10317/9309
AB - Increasingly more patients exposed to radiation from computed axial tomography (CT) will have a greater risk of developing tumors or cancer that are caused by cell mutation in the future. A minor dose level would decrease the number of these possible cases. However, this framework can result in medical specialists (radiologists) not being able to detect anomalies or lesions. This work explores a way of addressing these concerns, achieving the reduction of unnecessary radiation without compromising the diagnosis. We contribute with a novel methodology in the CT area to predict the precise radiation that a patient should be given to accomplish this goal. Specifically, from a real dataset composed of the dose data of over fifty thousand patients that have been classified into standardized protocols (skull, abdomen, thorax, pelvis, etc.), we eliminate atypical information (outliers), to later generate regression curves employing diverse well-known Machine Learning techniques. As a result, we have chosen the best analytical technique per protocol; a selection that was thoroughly carried out according to traditional dosimetry parameters to accurately quantify the dose level that the radiologist should apply in each CT test.
KW - Ingeniería Eléctrica
KW - Química-Física
KW - Tecnología Electrónica
KW - Machine learning
KW - Dose
KW - Computed axial tomography
KW - Patients
KW - 2301 Química Analítica
LA - eng
PB - MDPI
ER -