Bibliographic record
Abstract
This review introduces the current technique of photoacoustic imaging as it is applied in imaging-guided surgery (IGS), which provides the surgeon with image visualization and analysis capabilities during surgery. Numerous imaging techniques have been developed to help surgeons perform complex operations more safely and quickly. Although surgeons typically use these kinds of images to visualize targets hidden by bone and other tissues, it is nonetheless more difficult to perform surgery with static reference images (e.g., computed tomography scans and magnetic resonance images) of internal structures. Photoacoustic imaging could enable real-time visualization of regions of interest during surgery. Several researchers have shown that photoacoustic imaging has potential for the noninvasive diagnosis of various types of tissues, including bone. Previous studies of the surgical application of photoacoustic imaging have focused on cancer surgery, but photoacoustic imaging has also recently attracted interest for spinal surgery, because it could be useful for avoiding pedicle breaches and for choosing an appropriate starting point before drilling or pedicle probe insertion. This review describes the current instruments and clinical applications of photoacoustic imaging. Its primary objective is to provide a comprehensive overview of photoacoustic IGS in spinal surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".