<i>In vivo</i> quantification of fluorescent molecular markers in real‐time by ratio imaging for diagnostic screening and image‐guided surgery
Bibliographic record
Abstract
Future applications of "molecular diagnostic screening" and "molecular image-guided surgery" will demand images of molecular markers with high resolution and high throughput (~ > or =30 frames/second). MRI, SPECT, PET, optical fluorescence tomography, hyper-spectral fluorescence imaging, and bioluminescence imaging do not offer such high frame rates. 2D optical fluorescence imaging can provide surface images with high resolution and high throughput. The ability to accurately quantify the fluorescence in vivo is critical to extract functional information of the disease state, however few methods are available. Here, a ratiometric 2D quantification method is introduced. Through mathematical modeling the performance was evaluated using optical properties that resembled biological tissues with the fluorescent marker Protoporhyrin IX. Experimentally the performance was evaluated in optical phantoms with different optical properties employing a novel prototype clinical imaging system. The clinical feasibility of real-time, image-guided surgery was demonstrated in patients undergoing prostatectomy. Discussed are the reasons why the introduced method leads to an increased quantification performance followed by modifications so it can be applied to novel fluorescent molecular markers as phthalocyanine 4 and dual-fluorescent markers. These offer additional advantages as these can provide a linear response to marker concentration and further minimize the dependence on autofluorescence and optical properties, as demonstrated through modeling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".