Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests.
Sensors (Basel)
; 19(23)2019 Nov 22.
Article
em En
| MEDLINE
| ID: mdl-31766708
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tomografia Computadorizada por Raios X
Tipo de estudo:
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Espanha