Improved photoacoustic dosimetry for retinal laser surgery
Bibliographic record
Abstract
Lasers are employed for numerous medical interventions by exploiting ablative, disruptive or thermal effects. In ocular procedures, lasers have been used for decades to treat diseases such as diabetic retinopathy, macular edema and aged related macular degeneration via photocoagulation of retinal tissues. Although laser photocoagulation is well established in today’s practice, efforts to improve clinical outcomes by reducing the collateral damage from thermal diffusion is leading to novel treatments using shorter (μs) laser pulses (e.g. selective retinal therapy) which result in physical rather than thermal damage. However, for these new techniques to be widely utilized, a method is required to ensure safe but sufficient dosage has been applied, since no visible effects can be seen by ophthalmoscopy directly post treatment. Photoacoustic feedback presents an attractive solution, as the signal is dependent directly on absorbed dosage. Here, we present a method that takes advantage of temporal pulse formatting technology to minimize variation in absorbed dose in ophthalmic laser treatment and provide intelligent dosimetry feedback based on photoacoustic (PA) response. This method tailors the pulse to match the frequency response of the sample and/or detection chain. Depending on the system, this may include the absorbing particle size, the laser beam diameter, the laser pulse duration, tissue acoustic properties and the acoustic detector frequency response. A significant improvement (<7x) of photoacoustic signal-to-noise ratio over equivalent traditional pulse formats have been achieved, while spectral analysis of the detected signal provides indications of cavitation events and other sample properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".