Machine learning for real-time optical property recovery in interstitial photodynamic therapy: a stimulation-based study
Bibliographic record
Abstract
With the continued development of non-toxic photosensitizer drugs, interstitial photodynamic therapy (iPDT) is showing more favorable outcomes in recent clinical trials. IPDT planning is crucial to further increase the treatment efficacy. However, it remains a major challenge to generate a high-quality, patient-specific plan due to uncertainty in tissue optical properties (OPs), µ a and µ s . These parameters govern how light propagates inside tissues, and any deviation from the planning-assumed values during treatment could significantly affect the treatment outcome. In this work, we increase the robustness of iPDT against OP variations by using machine learning models to recover the patient-specific OPs from light dosimetry measurements and then re-optimizing the diffusers’ optical powers to adapt to these OPs in real time. Simulations on virtual brain tumor models show that reoptimizing the power allocation with the recovered OPs significantly reduces uncertainty in the predicted light dosimetry for all tissues involved.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".