Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests.
Antonio-Javier Garcia-SanchezEnrique Garcia AngostoJose Luis LlorAlfredo Serna BernaDavid RamosPublished in: Sensors (Basel, Switzerland) (2019)
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.
Keyphrases
- computed tomography
- machine learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- image quality
- chronic kidney disease
- dual energy
- positron emission tomography
- magnetic resonance imaging
- healthcare
- peritoneal dialysis
- artificial intelligence
- randomized controlled trial
- patient reported outcomes
- radiation induced
- electronic health record
- mass spectrometry