Fully Automated Agatston score calculation from ECG gated Cardiac CT using Deep learning and Multiorgan Segmentation: A Validation study
Bibliographic record
Abstract
Abstract Purpose: To evaluate deep learning-based calcium segmentation and quantification on ECG gated Cardiac CT scans compared with manual evaluation. Methods:Automated calcium quantification was performed using a combination of deep-learning convolution neural networks based on Mask R CNN for multiorgan segmentation. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using 2D erosion, dilation, volume, and maximum intensity threshold and by applying cardiac, aortic, and epicardial fat segmentation. This study used 40 patients to train and test the segmentation model. Results:110 patients were tested for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores on the test set show high level of correlation (0.84; p < 0.001) and high limits of agreement in Bland-Altman plot. The proposed method correctly classifies the risk group in 75.2% of the cases and classifies the subjects in the same group. 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90 % and a specificity of 94 %. Conclusion: Fully automated deep learning-based calcium quantification on cardiac-CTs shows good correlation compared to reference standards. Automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional resources.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".