AB123. SOH25_AB_365. Quantitative analysis of indocyanine green fluorescence angiography in colorectal surgery: a systematic review of the literature
Bibliographic record
Abstract
Background: Indocyanine green fluorescence angiography (ICGFA) during colorectal surgery is associated with reduced rates of post-operative anastomotic complications. Because interpretation of ICGFA is subjective, quantification has been proposed to address inter-user variability. This study reviews the published literature regarding ICGFA quantification during colorectal surgery with a focus on clinical deployment. Methods: A systematic review of English language publications was performed in PubMed, Scopus, Web of Science and Cochrane Library on 29th August 2024, updated to 18th November 2024 following PRISMA guidelines. All clinical studies referencing ICGFA quantification during colorectal surgery were included with the Newcastle Ottawa scale used to assess quality. Results: A total of 1,428 studies were screened, resulting in the selection of 22 studies (1,469 patients). Significant heterogeneity of ICGFA methodology, quantification methods, and parameter selection was noted with only three being scored “high” quality. Four studies (154 patients) conducted real-time analyses of the fluorescence signal (others were post-hoc analyses) and four utilised artificial intelligence methods. Eleven studies only included patients undergoing left-sided resection, with six of these focusing specifically on rectal resections. Only one study employed the quantification method to guide intra-operative decision-making regarding transection of the colonic segment. Twenty-six different perfusion parameters were assessed, with time from injection to visible fluorescence and maximum intensity the most commonly (but not only) correlated parameters regarding anastomotic complication. Conclusions: Quantification of the ICGFA signal for colorectal surgery is feasible but has so far seen limited academic advancement beyond feasibility.
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 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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".