HKA-Net: clinically-adapted deep learning for automated measurement of hip-knee-ankle angle on lower limb radiography for knee osteoarthritis assessment
The proposed ResNet-based model provides an efficient and accurate method for measuring HKA angles and classifying varus deformity stages without the need for extensive landmark annotations. Its high accuracy and significant improvement in time efficiency make it a valuable tool for clinical practice, potentially enhancing decision-making and workflow efficiency in the management of knee OA.
Authors
- Beom-Su Han
- Jae‐Ho Han (ORCID: https://orcid.org/0000-0002-6490-9825)
- Ji Seon Chae (ORCID: https://orcid.org/0000-0002-5914-678X)
- Yunsik Son (ORCID: https://orcid.org/0000-0002-2580-4393)
- K. Jason (ORCID: https://orcid.org/0000-0002-3251-5004)
- Jung-Kwon Bae (ORCID: https://orcid.org/0000-0002-9235-1347)
- Je Hyeong Hong (ORCID: https://orcid.org/0000-0003-2797-553X)
- Synho Do (ORCID: https://orcid.org/0000-0001-6211-7050)
- Jung Bin Kim (ORCID: https://orcid.org/0000-0002-8013-9349)
- Young-Tak Kim (ORCID: https://orcid.org/0000-0003-3604-6580)
Institutions
- Harvard University (US)
- Ewha Womans University (KR)
- Korea University (KR)
- Dongguk University (KR)
- Seoul Medical Center (KR)
- Massachusetts General Hospital (US)
- Harvard Stem Cell Institute (US)
- Korea University (JP)
- Hanyang University (KR)
- Anyang University (KR)
Publication Details
- Journal
- Journal of Orthopaedic Surgery and Research
- Published
- 2024-11-20
- DOI
- https://doi.org/10.1186/s13018-024-05265-y
- Citations
- 3
- Primary Topic
- Total Knee Arthroplasty Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- National Research Foundation
- National Research Foundation of Korea
- Iran Telecommunication Research Center
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion