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Open AccessArticle

Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests

1
Department of Information and Communication Technologies, Universidad Politécnica de Cartagena (UPCT), Campus Muralla del Mar, E-30202 Cartagena, Spain
2
General Electric Healthcare, E-28023 Madrid, Spain
3
Hospital General Universitario Santa Lucía, E-30202 Cartagena, Spain
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5116; https://doi.org/10.3390/s19235116
Received: 17 September 2019 / Revised: 13 November 2019 / Accepted: 20 November 2019 / Published: 22 November 2019
(This article belongs to the Special Issue Multimodal Data Fusion and Machine-Learning for Healthcare)
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. View Full-Text
Keywords: machine learning; dose; computed axial tomography; patients machine learning; dose; computed axial tomography; patients
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MDPI and ACS Style

Garcia-Sanchez, A.-J.; Garcia Angosto, E.; Llor, J.L.; Serna Berna, A.; Ramos, D. Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests. Sensors 2019, 19, 5116.

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