A Novel and Generic Workflow of Indocyanine Green Perfusion Assessment Integrating Standardization and Quantification Toward Clinical Implementation
Bibliographic record
Abstract
OBJECTIVE: This study aims to generate a reproducible and generalizable Workflow model of ICG-angiography integrating Standardization and Quantification (WISQ) that can be applied uniformly within the surgical innovation realm independent of the user. SUMMARY BACKGROUND DATA: Tissue perfusion based on indocyanine green (ICG)-angiography is a rapidly growing application in surgical innovation. Interpretation of results has been subjective and error-prone due to the lack of a standardized and quantitative ICG-workflow and analytical methodology. There is a clinical need for a more generic, reproducible, and quantitative ICG perfusion model for objective assessment of tissue perfusion. METHODS: In this multicenter, proof-of-concept study, we present a generic and reproducible ICG-workflow integrating standardization and quantification for perfusion assessment. To evaluate our model's clinical feasibility and reproducibility, we assessed the viability of parathyroid glands after performing thyroidectomy. Biochemical hypoparathyroidism was used as the postoperative endpoint and its correlation with ICG quantification intraoperatively. Parathyroid gland is an ideal model as parathyroid function post-surgery is only affected by perfusion. RESULTS: We show that visual subjective interpretation of ICG-angiography by experienced surgeons on parathyroid perfusion cannot reliably predict organ function impairment postoperatively, emphasizing the importance of an ICG quantification model. WISQ was able to standardize and quantify ICG-angiography and provided a robust and reproducible perfusion curve analysis. A low ingress slope of the perfusion curve combined with a compromised egress slope was indicative for parathyroid organ dysfunction in 100% of the cases. CONCLUSION: WISQ needs prospective validation in larger series and may eventually support clinical decision-making to predict and prevent postoperative organ function impairment in a large and varied surgical population.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".