External Validation of a Novel Landmark-Based Deep Learning Automated Tibial Slope Measurement Algorithm Applied on Short Radiographs Obtained in Patients With ACL Injuries
In this external validation of a newly developed model for automated readings of tibial slope measures, a perfect intrarater reliability and a good interrater reliability were seen. Although the model needs further refinement in reporting the tibial slope as compared with a gold standard manual measurement, it clearly demonstrates the elimination of human variance with repeat readings and less time consumption than that with human effort.
Authors
- R. Kyle Martin (ORCID: https://orcid.org/0000-0001-9918-0264)
- Andreas Persson (ORCID: https://orcid.org/0000-0003-1409-9112)
- Eivind Inderhaug (ORCID: https://orcid.org/0000-0001-7959-163X)
- S. Haaland (ORCID: https://orcid.org/0000-0002-1241-7570)
- Byung-Han Ryu (ORCID: https://orcid.org/0000-0001-7790-7645)
- Jun Woo Nam
- Du Hyun Ro (ORCID: https://orcid.org/0000-0001-6199-908X)
- Sung Eun Kim (ORCID: https://orcid.org/0000-0002-1954-9875)
Institutions
- Oslo University Hospital (NO)
- University of Minnesota (US)
- Haukeland University Hospital (NO)
- Seoul National University Hospital (KR)
- Oslo Sports Trauma Research Center (NO)
- Haraldsplass Diakonale Sykehus (NO)
- University of Bergen (NO)
Publication Details
- Journal
- Orthopaedic Journal of Sports Medicine
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/23259671251333607
- Citations
- 2
- Primary Topic
- Knee injuries and reconstruction techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.01