핵의학 감마카메라 정도관리의 딥러닝 적용

• 영문명: Deep Learning Application of Gamma Camera Quality Control in Nuclear Medicine
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 정의환(Euihwan Jeong) 오주영(Joo-Young Oh) 이주영(Joo-Young Lee) 박훈희(Hoon-Hee Park)
• 간행물 정보: 『방사선기술과학』방사선기술과학 제43권 제6호, 461~467쪽, 전체 7쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2020.12.31

영문 초록

In the field of nuclear medicine, errors are sometimes generated because the assessment of the uniformity of gamma cameras relies on the naked eye of the evaluator. To minimize these errors, we created an artificial intelligence model based on CNN algorithm and wanted to assess its usefulness. We produced 20,000 normal images and partial cold region images using Python, and conducted artificial intelligence training with Resnet18 models. The training results showed that accuracy, specificity and sensitivity were 95.01%, 92.30%, and 97.73%, respectively. According to the results of the evaluation of the confusion matrix of artificial intelligence and expert groups, artificial intelligence was accuracy, specificity and sensitivity of 94.00%, 91.50%, and 96.80%, respectively, and expert groups was accuracy, specificity and sensitivity of 69.00%, 64.00%, and 74.00%, respectively. The results showed that artificial intelligence was better than expert groups. In addition, by checking together with the radiological technologist and AI, errors that may occur during the quality control process can be reduced, providing a better examination environment for patients, providing convenience to radiologists, and improving work efficiency.

목차

Ⅰ. 서 론

Ⅱ. 대상 및 방법

Ⅲ. 결 과

Ⅳ. 고 찰

Ⅴ. 결 론

REFERENCES

키워드

핵의학 정도관리 인공지능 콘볼루션 신경망 딥러닝 Nuclear medicine Quality Control AI CNN Deep Learning