In silico experiments of time-resolved near-infrared light transport through human fingers with simulated rheumatoid arthritis
Bibliographic record
Abstract
Diffuse optical techniques have been used to assess joint inflammation in rheumatoid arthritis (RA); however, there is scant data on the use of time-domain (TD) techniques for this purpose. We conducted TD simulations in a realistic finger joint and investigated how TD parameters changed in response to simulated joint inflammation. An MRI image of a healthy human finger was segmented into 7 tissue types and manipulated to create 4 models representing various degrees of RA disease activity. NIRFAST was used to simulate TD light propagation through the models at 800nm using an array of light sources/detectors placed across the proximal interphalangeal (PIP) joint. Parameters were extracted from the resulting TPSFs using statistical moments (number of photons, mean time-of-flight, centralized variance), temporal binning (early, middle, late temporal windows), and Fourier decomposition to produce a series of 2D images. Spatial frequency components were then extracted from each of the images and used to detect differences between the 4 models of disease activity. Spatial frequency amplitudes of high-frequency temporal Fourier component images differentiated between models with 100% accuracy and with the highest percent differences (i.e., performance). However, once Poisson noise was added to the TPSFs, amplitudes of early photon and total number of photon images at the 0.14mm−1 spatial frequency had the highest performance for accuracy cut-offs of 95% and 100%, respectively. Future work will focus on validating results with experimental TD-DOI data and assessing how combinations of various parameters can be used to increase performance while maintaining accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".