Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: experimental validation in tissue-mimicking finger phantoms
Bibliographic record
Abstract
Abstract Objective. Effective treatment within 3–5 months of disease onset significantly improves rheumatoid arthritis (RA) prognosis. Nevertheless, 30% of RA patients fail their first treatment, and it takes 3–6 months to identify failure with current monitoring techniques. Time-domain diffuse optical imaging (TD-DOI) may be more sensitive to RA disease activity and could be used to detect treatment failure. In this report, we present the development of a TD-DOI hand imaging system and validate its ability to measure simulated changes in RA disease activity using tissue-mimicking finger phantoms. Approach. A TD-DOI system was built, based on a single-pixel camera architecture, and used to image solid phantoms which mimicked a proximal interphalangeal finger joint. For reference, in silico images of virtual models of the solid phantoms were also generated using Monte Carlo simulations. Spatiotemporal Fourier components were extracted from both simulated and experimental images, and their ability to distinguish between phantoms representing different RA disease activity was quantified. Main results. Many spatiotemporal Fourier components extracted from TD-DOI images could clearly distinguish between phantoms representing different states of RA disease activity. Significance. A TD-DOI system was built and validated using finger-mimicking solid phantoms. The findings suggest that the system could be used to monitor RA disease activity. This single-pixel TD-DOI system could be used to acquire longitudinal measures of RA disease activity to detect early treatment failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".