Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests
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AuthorGarcía Sánchez, Antonio Javier; García Angosto, Enrique Ángel; Llor Álvarez, José Luis; Serna Berna, Alfredo; Ramos Amores, David
Knowledge AreaIngeniería EléctricaQuímica-FísicaTecnología Electrónica
SponsorsThis research has been supported by the projects AIM, ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE), e-DIVITA, ref.20509/PDC/18 (Proof of Concept, 2018) and ATENTO, ref. 20889/PI/18 (Fundación Seneca, Región de Murcia).
Realizado en/conUniversidad Politécnica de Cartagena; General Electric Healthcare; Hospital General Universitario Santa Lucía
Bibliographic CitationGarcia-Sanchez A-J, Garcia Angosto E, Llor JL, Serna Berna A, Ramos D. Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests. Sensors. 2019; 19(23):5116. https://doi.org/10.3390/s19235116
Computed axial tomography
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, ...
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