ArticleVisual computing for industry, biomedicine, and art2025
Radiographic prediction model based on X-rays predicting anterior cruciate ligament function in patients with knee osteoarthritis.
Article in Visual computing for industry, biomedicine, and art, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
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6 authors.
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Abstract
Knee osteoarthritis (KOA) is a prevalent chronic condition in the elderly and is often associated with instability caused by anterior cruciate ligament (ACL) degeneration. The functional integrity of ACL is crucial for the diagnosis and treatment of KOA. Radiographic imaging is a practical diagnostic tool for predicting the functional status of the ACL. However, the precision of the current evaluation methodologies remains suboptimal. Consequently, we aimed to identify additional radiographic features from X-ray images that could predict the ACL function in a larger cohort of patients with KOA. A retrospective analysis was conducted on 272 patients whose ACL function was verified intraoperatively between October 2021 and October 2024. The patients were categorized into ACL-functional and ACL-dysfunctional groups. Using least absolute shrinkage and selection operator regression and logistic regression, four significant radiographic predictors were identified: location of the deepest wear on the medial tibial plateau (middle and posterior), wear depth in the posterior third of the medial tibial plateau (> 1.40 mm), posterior tibial slope (PTS > 7.90°), and static anterior tibial translation (> 4.49 mm). A clinical prediction model was developed and visualized using a nomogram with calibration curves and receiver operating characteristic analysis to confirm the model performance. The prediction model demonstrated great discriminative ability, showing area under the curve values of 0.831 (88.4% sensitivity, 63.8% specificity) and 0.907 (86.1% sensitivity, 82.2% specificity) in the training and validation cohorts, respectively. Consequently, the authors established an efficient approach for accurate evaluation of ACL function in KOA patients.
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