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], Pierce TM 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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".