Potential of AI-based diagnostic grading system for knee osteoarthritis
The proposed AI-based CAD system shows promise for supporting KOA diagnosis and grading in clinical practice, particularly for training junior clinicians and radiologists. Despite limitations of dataset imbalance and restricted single-center data, transfer learning with DenseNet-121 achieved reliable performance. Future work should focus on expanding datasets to encompass diverse populations, incorporating multimodal inputs, and validating generalizability across various clinical settings. This approach highlights the growing role of AI in musculoskeletal imaging and its potential to enhance early disease detection and patient care.
Authors
- Shahid Zikria (ORCID: https://orcid.org/0000-0001-7414-9826)
- Saman Shahid (ORCID: https://orcid.org/0000-0001-5274-3031)
- Onur Osman (ORCID: https://orcid.org/0000-0001-7675-7999)
- Aamir Wali (ORCID: https://orcid.org/0000-0002-5314-6113)
- Jawad Rasheed (ORCID: https://orcid.org/0000-0003-3761-1641)
- Aatir Javaid (ORCID: https://orcid.org/0009-0008-0685-5006)
Institutions
- İstanbul Sabahattin Zaim Üniversitesi (TR)
- Information Technology University (PK)
- University of Lahore (PK)
- Applied Science Private University (JO)
- National University of Computer and Emerging Sciences (PK)
- İstanbul Nişantaşı Üniversitesi (TR)
- Istanbul Medipol University (TR)
- Istanbul University (TR)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-10-30
- DOI
- https://doi.org/10.3389/fmed.2025.1707588
- Citations
- 2
- Primary Topic
- Osteoarthritis Treatment and Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 3.34
Funders
- National University of Computer and Emerging Sciences