Abstract 15522: Segmentation Improves Deep Learning Accuracy for Differentiating Non-Ischemic and Ischemic Cardiomyopathy Using Cardiac Mri Cine Imaging
Bibliographic record
Abstract
Background: Deep learning (DL) models typically interpret images without prior knowledge of anatomic significance. However, pathophysiology is highly classified by human definitions according to underlying affected anatomy. Therefore, we examine the impact of introducing explicit knowledge of anatomy through cardiac contours on cardiac magnetic resonance images (CMR) to DL models. The DL models were then trained to differentiate between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) using late gadolinium enhanced (LGE) CMR. Method: We evaluated 301 CMR studies; ICM (n=176) and NICM (n=125). Manual contouring of LGE images was performed using CVI42 (Circle, Ontario). We compared a radiomic and end-to-end DL approach to identify cardiomyopathy (CM) etiology from short axis LGE images. Patients are randomly assigned to training (70%) and testing (30%). Model performance was assessed with area under curve (AUC). Result: Table 1 shows the results of radiomic and deep learning approaches to differentiate NICM and ICM. Segmented/manually contoured images with removal of non-cardiac structures greatly improved classification accuracy across all deep learning models. The average improvement in AUC was 0.163 when using segmented images compared to the full images. Furthermore, the deep learning models outperformed the radiomics models. The best radiomic model and deep learning model achieved AUCs of 0.914 and 0.947, respectively. Both radiomic based models achieved AUCs above 0.874 while all 2D deep learning models with segmented images achieved AUCs above 0.875. The two 3D deep learning models which utilize 3D convolutions provided lower AUCs ranging between 0.743 and 0.900. Conclusion: Manual segmentation of LGE images improved the ability to train DL models with fairly small volumes of labeled data, resulting in higher classification accuracy. Table1 AUCs of various models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".