Angiography-Based Detection of Coronary Artery Stenosis Using YOLO Algorithm
Bibliographic record
Abstract
This study investigates the efficacy of You Only Look Once (YOLO) algorithms in detecting coronary artery stenosis from angiographic images.The dataset utilized comprises 8,325 grayscale images sourced from publicly available databases, featuring patients diagnosed with single-vessel coronary artery disease.An expert cardiologist annotated the images to precisely mark areas of vascular occlusion, providing reliable training data.Four distinct datasets were constructed and divided into training (80%) and testing (20%) subsets.YOLO v5, v7, and v8 models were trained over 100 epochs to evaluate their performance in identifying stenotic regions.The study emphasizes the advantages of YOLO algorithms, particularly their ability to detect multiple objects in real-time with high accuracy, due to their single-stage detection architecture.Performance metrics such as Mean Average Precision (MAP), precision, recall, and F1-score were computed to assess model effectiveness.The results demonstrate that YOLO v5 and YOLO v8 provide robust detection capabilities, outperforming YOLO v7, especially in complex image scenarios.This research highlights the potential integration of YOLO models in clinical workflows, offering a rapid and accurate tool for automated analysis of coronary artery stenosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".