Application of Vision-Language Models for Assessing Osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) poses a global health challenge, demanding precise diagnostic methods. Current radiographic assessments are time-consuming and prone to variability, prompting the need for automated solutions. The existing deep learning models for OA assessment are unimodal single-task systems and they don't incorporate relevant text information such as patient demographics, disease history, or physician reports. This study investigates employing Vision-Language Processing (VLP) models to predict OA severity using X-ray images and corresponding reports. Our method leverages X-ray images of the knee and diverse report templates generated from tabular OA scoring values to train a CLIP (Contrastive Language-Image Pre-Training) style VLP model. Furthermore, we incorporate additional contrasting captions to enforce the model to discriminate between positive and negative reports. Results demonstrate the efficacy of these models in learning text-image representations and their contextual relationships, showcase potential advancement in OA assessment, and establish a foundation for specialized vision-language models in medical contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".