Enhancing diagnosis of gout with deep learning in dual-energy computed tomography: a retrospective analysis of crystal and artefact differentiation
Our study demonstrates the effectiveness of the DL algorithm in differentiating between green colour coding indicative of crystal deposition and clumpy artefacts in DECT scans. With high sensitivity, specificity, and accuracy, the utilization of DL in DECT for diagnosing gout enables precise lesion classification, facilitating early-stage diagnosis and promoting timely intervention approaches.
Authors
- Younhee Choi (ORCID: https://orcid.org/0000-0003-2124-3848)
- Gong Yong Jin (ORCID: https://orcid.org/0000-0002-1426-554X)
- Jae Sung Yun
- Riel Castro‐Zunti (ORCID: https://orcid.org/0000-0002-3977-8958)
- Seok‐Bum Ko (ORCID: https://orcid.org/0000-0002-9287-317X)
- Wan‐Hee Yoo (ORCID: https://orcid.org/0000-0002-8430-7629)
- Eun Jung Choi (ORCID: https://orcid.org/0000-0002-2339-4327)
- Eun Hae Park (ORCID: https://orcid.org/0000-0001-9901-6333)
- Yun‐Jung Choi
Institutions
- University of Saskatchewan (CA)
- Jeonbuk National University Hospital (KR)
- Ajou University (KR)
- Jeonbuk National University (KR)
Publication Details
- Journal
- Lara D. Veeken
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1093/rheumatology/keae523
- Citations
- 2
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.37