Near-infrared Fluorescence Optical Imaging in Early Rheumatoid Arthritis: A Comparison to Magnetic Resonance Imaging and Ultrasonography
Bibliographic record
Abstract
OBJECTIVE: Near-infrared fluorescence optical imaging (FOI) is a novel imaging technology in the detection and evaluation of different arthritides. FOI was validated in comparison to magnetic resonance imaging (MRI), greyscale ultrasonography (GSUS), and power Doppler ultrasonography (PDUS) in patients with early rheumatoid arthritis (RA). METHODS: Hands of 31 patients with early RA were examined by FOI, MRI, and US. In each modality, synovitis of the wrist, metacarpophalangeal joints (MCP) 2-5, and proximal interphalangeal joints (PIP) 2-5 were scored on a 4-point scale (0-3). Sensitivity and specificity of FOI were analyzed in comparison to MRI and US as reference methods, differentiating between 3 phases of FOI enhancement (P1-3). Intraclass correlation coefficients (ICC) were calculated to evaluate the agreement of FOI with MRI and US. RESULTS: A total of 279 joints (31 wrists, 124 MCP and 124 PIP joints) were evaluated. With MRI as the reference method, overall sensitivity/specificity of FOI was 0.81/0.00, 0.49/0.84, and 0.86/0.38 for wrist, MCP, and PIP joints, respectively. Under application of PDUS as reference, sensitivity was even higher, while specificity turned out to be low, except for MCP joints (0.88/0.15, 0.81/0.76, and 1.00/0.27, respectively). P2 appears to be the most sensitive FOI phase, while P1 showed the highest specificity. The best agreement of FOI was shown for PDUS, especially with regard to MCP and PIP joints (ICC of 0.57 and 0.53, respectively), while correlation with MRI was slightly lower. CONCLUSION: FOI remains an interesting diagnostic tool for patients with early RA, although this study revealed limitations concerning the detection of synovitis. Further research is needed to evaluate its full diagnostic potential in rheumatic diseases.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".