머신러닝 기반 신체 계측정보를 이용한 CT 피폭선량 예측모델 비교

• 영문명: Comparison of CT Exposure Dose Prediction Models Using Machine Learning-based Body Measurement Information
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 홍동희(Dong-Hee Hong)
• 간행물 정보: 『방사선기술과학』방사선기술과학 제43권 제6호, 503~509쪽, 전체 7쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2020.12.31

영문 초록

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient s anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.

목차

Ⅰ. 서 론

Ⅱ. 연구방법

Ⅲ. 결 과

Ⅳ. 고 찰

Ⅴ. 결 론

REFERENCES

키워드

머신러닝 랜덤포레스트 선형회귀분석 서포트벡터머신 피폭선량 Machine learning Random forest Linear regression Support vector machine Exposure