mlsanityData to 7 Oct 2026

Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning

arXiv 2201.10849 ↗First posted 26 Jan 2022eess.IV cs.CVcited by 4

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Abstract (arXiv)

Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials. Prior art focused on manually designed imaging biomarkers, which may not fully exploit all disease-related information present in MRI scan. In contrast, our method learns relevant representations from raw data end-to-end using Deep Learning, and uses them for progression prediction. The method employs a 2D CNN to process the data slice-wise and aggregate the extracted features using a Transformer. Evaluated on a large cohort (n=4,866), the proposed method outperforms conventional 2D and 3D CNN-based models and achieves average precision of 0.58±0.03 and ROC AUC of 0.78±0.01. This paper sets a baseline on end-to-end KOA progression prediction from structural MRI. Our code is publicly available at https://github.com/MIPT-Oulu/OAProgressionMR.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 26 Jan 2022.

Builds on

15 of 24 references.

References · 15

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3 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2023End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformerscited by 1
  2. 2024Toward Cost-efficient Adaptive Clinical Trials in Knee Osteoarthritis with Reinforcement Learningcited by 0
  3. 2025Downstream Analysis of Foundational Medical Vision Models for Disease Progressioncited by 0
  4. 2025Improving Generalization in MRI-Based Deep Learning Models for Total Knee Replacement Predictioncited by 0