http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : May Sadik (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
May Sadik 대한핵의학회 2023 핵의학 분자영상 Vol.57 No.2
Purpose Classification of focal skeleton/bone marrow uptake (BMU) can be challenging. The aim is to investigate whether anartificial intelligence–based method (AI), which highlights suspicious focal BMU, increases interobserver agreement amonga group of physicians from different hospitals classifying Hodgkin’s lymphoma (HL) patients staged with [18F]FDG PET/CT. Methods Forty-eight patients staged with [18F]FDG PET/CT at Sahlgenska University Hospital between 2017 and 2018 werereviewed twice, 6 months apart, regarding focal BMU. During the second time review, the 10 physicians also had access toAI-based advice regarding focal BMU. Results Each physician’s classifications were pairwise compared with the classifications made by all the other physicians,resulting in 45 unique pairs of comparisons both without and with AI advice. The agreement between the physicians increasedsignificantly when AI advice was available, which was measured as an increase in mean Kappa values from 0.51 (range0.25–0.80) without AI advice to 0.61 (range 0.19–0.94) with AI advice (p = 0.005). The majority of the physicians agreedwith the AI-based method in 40 (83%) of the 48 cases. Conclusion An AI-based method significantly increases interobserver agreement among physicians working at differenthospitals by highlighting suspicious focal BMU in HL patients staged with [18F]FDG PET/CT.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
