Effect of Deep Learning-Based Artificial Intelligence on Radiologists’ Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging
DL-based AI enhances the diagnostic performance in detecting N1 abnormalities on SMwI, particularly benefiting less experienced radiologists. These findings underscore the potential for improving diagnostic workflows for PD.
Authors
- Eung Yeop Kim (ORCID: https://orcid.org/0000-0002-9579-4098)
- Han Kyu Na (ORCID: https://orcid.org/0000-0002-2093-1641)
- Minjung Seong (ORCID: https://orcid.org/0000-0002-9257-4225)
- Phil Hyu Lee (ORCID: https://orcid.org/0000-0001-9931-8462)
- So Yeon Won (ORCID: https://orcid.org/0000-0003-0570-3365)
- Yun Hwa Roh (ORCID: https://orcid.org/0000-0002-8041-1621)
- Beomseok Sohn (ORCID: https://orcid.org/0000-0002-6765-8056)
- Yongsik Sim (ORCID: https://orcid.org/0000-0003-2711-2793)
- Sun‐Young Baek (ORCID: https://orcid.org/0000-0001-6519-7770)
- Sung Tae Kim (ORCID: https://orcid.org/0000-0001-8185-0063)
- Jiyeon Park (ORCID: https://orcid.org/0000-0002-0353-0109)
- Chae Young Lim (ORCID: https://orcid.org/0009-0000-7858-5430)
Institutions
- Yonsei University (KR)
- Severance Hospital (KR)
- Samsung Medical Center (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Korean Journal of Radiology
- Published
- 2025-01-01
- DOI
- https://doi.org/10.3348/kjr.2025.0208
- Citations
- 3
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 7.44