Development of a handheld diffuse optical breast cancer assessment probe
Bibliographic record
Abstract
Diffuse Optical Spectroscopy (DOS) is a promising non-invasive and non-ionizing technique for breast anomaly detection. In this study, we have developed a new handheld DOS probe to measure optical properties of breast tissue. In the proposed probe, the breast tissue is illuminated with four near infrared (NIR) wavelengths light emitting diodes (LED), which are encapsulated in a package (eLEDs), and two PIN photodiodes measure the intensity of the scattered photons at two different locations. The proposed technique of using eLEDs is introduced, in order to have a multi-wavelength pointed-beam illumination source instead of using the laser-coupled fiber-optic technique, which increases the complexity, size, and cost of the probe. Despite the fact that the proposed technique miniaturizes the probe and reduces the complexity of the DOS, the study proves that it is accurate and reliable in measuring optical properties of the tissue. The measurements are performed at the rate of 10[Formula: see text]Hz which is suitable for dynamic measurement of biological activity, in-vivo. The multi-spectral evaluation algorithm is used to reconstruct four main absorber concentrations in the breast including oxy-hemoglobin (cHb), deoxy-hemoglobin (cHbO2), water (cH2O), fat (cFat), and average scattering coefficient of the medium, as well as concentration changes in Hb ([Formula: see text]cHb) and HbO2 ([Formula: see text]cHbO2). Although the probe is designed for breast cancer diagnosis, it can be used in a wide range of applications for both static and dynamic measurements such as functional brain imaging. A series of phantoms, comprised of Delrin[Formula: see text], Intralipid[Formula: see text], PierceTM and Black ink, are used to verify performance of the device. The probe will be tested on human subjects, in-vivo, in the next phase.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".