Abstract 12691: Comparison of Deep Learning and Radiomic Features to Differentiate Non-Ischemic and Ischemic Cardiomyopathy Using Cardiac MRI Cine Imaging
Bibliographic record
Abstract
Introduction: Cardiac magnetic resonance (CMR) is frequently utilized to characterize etiology of cardiomyopathy (CM), but there is need for improved disease classification, standardization in the interpretation of findings, and throughput in analysis. Radiomics has been shown to classify disease in a semi-automated manner. Alternatively, deep learning (DL) provides the ability to identify unknown features in image data. Therefore, we sought to compare DL and radiomic approaches to differentiate ischemic vs non-ischemic cardiomyopathy (ICM vs NICM), using cardiac magnetic resonance (CMR) short axis cine images. Methods: We selected 291 patients with cardiomyopathy (CM) who underwent a CMR exam at Cleveland Clinic between 2008 and 2018, of which 249 had NICM (positive label) based on expert review of the CMR exam and electronic medical record documentation. We compared a radiomic and end-to-end DL approach to identify CM etiology from short axis cine images. Automatically generated radiomic features describing myocardial shape, texture, thickness, and motion in the cine images were used to train several machine learning classifiers. In the DL approach, we directly used the cine images to train several DL classifiers, without extracting radiomic features. We evaluated the classifiers through 5-fold cross validation using the area under the curve (AUC), F1-score, and accuracy metrics. Statistical significance was evaluated using paired 2-tailed t-test at 0.05 level. Results: Support vector machine (SVM) and DenseNet121 achieved the best metrics for radiomic and DL approaches respectively. The radiomic and DL approach achieved similar AUCs of 0.852 and 0.858 respectively, but DL approach achieved statistically significant higher F1-score of 0.758 vs 0.585 of the radiomic approach. Conclusions: An end-to-end DL approach more accurately identified NICM vs ICM compared to a radiomics approach, using only cine CMR images.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".