Standardizing image acquisition and processing methods: a critical need for the accurate assessment of retinal blood vessel tortuosity
Bibliographic record
Abstract
Retinal vascular tortuosity is a biomarker associated with various retinal and systemic diseases. However, its clinical application is hindered by the lack of an objective, standardized definition. With the growing number of automated pipelines for the evaluation of tortuosity from retinal images, the impact of image processing, choice of tortuosity index, and retinal camera imaging parameters have been overlooked. This study assesses the robustness of different tortuosity indices to artificial changes in camera imaging parameters and the choice of centerline extraction algorithm to provide a path toward standardization of tortuosity quantification. Five tortuosity indices were implemented and tested on tortuosity-ordered vessel segments using Spearman's Rank Correlation Coefficients for comparison. Comparable results were obtained for all tested indices. Image magnification was found to have minor or no impact on the most tested indices. A key finding from this study is that the same blood vessel imaged in different regions of the field of view of the same imaging system may differ in measured tortuosity. Different centerline extraction algorithms were also found to have a critical impact on tortuosity quantification. Taken as a whole, this study shows the importance of standardizing the analytical procedures in the evaluation of blood vessel tortuosity.
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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 | 0.001 | 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 | 0.001 |
| 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 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".