Deep Learning-Based Landmark Detection Model for Multiple Foot Deformity Classification: A Dual-Center Study
The HIH model showed robust performance in diagnosing multiple foot deformities with high efficacy in internal and external validation. Our approach is expected to be effective for various tasks using landmarks in medical imaging.
Authors
- S.M. Park (ORCID: https://orcid.org/0000-0002-0249-3617)
- Ji Cheol Shin (ORCID: https://orcid.org/0000-0002-1133-1361)
- Jong Hak Moon (ORCID: https://orcid.org/0000-0002-6708-3918)
- Inyoung Paik (ORCID: https://orcid.org/0009-0002-8082-022X)
- Edward Choi (ORCID: https://orcid.org/0000-0002-5958-3509)
- Seongsu Bae (ORCID: https://orcid.org/0000-0003-4482-8953)
- Chan Woong Jang (ORCID: https://orcid.org/0000-0002-5037-0080)
- Han Gyul Yoon (ORCID: https://orcid.org/0000-0002-9515-1623)
- Eunho Yang (ORCID: https://orcid.org/0000-0003-2188-0169)
- Su Ji Lee (ORCID: https://orcid.org/0000-0002-6376-0125)
Institutions
- Korea Advanced Institute of Science and Technology (KR)
- Yonsei University (KR)
- Gangnam Severance Hospital (KR)
- Hallym University Sacred Heart Hospital (KR)
Publication Details
- Journal
- Yonsei Medical Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.3349/ymj.2024.0246
- Primary Topic
- Lower Extremity Biomechanics and Pathologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Science and ICT, South Korea