Real‐Time, <scp>AI</scp>‐Guided Photodynamic Laparoscopy Enhances Detection in a Rabbit Model of Peritoneal Cancer Metastasis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate diagnosis is essential for effective cancer treatment, particularly in peritoneal surface malignancies, where failure to detect metastatic lesions can mislead the treatment plan. This study assessed the diagnostic accuracy of staging laparoscopy using the integration of artificial intelligence (AI)-guided photodynamic diagnosis (PDD) with the photosensitizer Phonozen, activated at 405 nm in a rabbit model. To create peritoneal carcinomatosis, VX2 cells were inoculated laparoscopically into the peritoneum of female white New Zealand rabbits. Conventional and PDD-guided laparoscopy utilized a customized light source that emitted broad-spectrum white light or 405-nm blue light, respectively. The surgical procedure comprised a tripartite approach: exploration and labeling of suspected nodules under white-light visualization, identification of additional metastatic tumors under blue-excitation fluorescent light, and confirmatory open laparotomy to locate overlooked nodules by palpation. Our results showed that the initial experimental data from 371 nodules in 14 rabbits, comparing conventional diagnostic laparoscopy and PDD, showed increased detection sensitivity from 67% ± 1.9% (conventional) to 98% ± 0.7% (PDD) in the small-size nodule. In the second experimental data set from 265 nodules in 10 rabbits, the addition of a real-time AI algorithm further increased the sensitivity to 100% ± 0.0%. Combining PDD with AI enhances the detection of peritoneal cancer metastasis in staging laparoscopy.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it