4941Machine learning based automated diagnosis of ischemic vs non-ischemic dilated cardiomyopathy using 3D myocardial deformation analysis
Bibliographic record
Abstract
Abstract Background Late Gadolinium Enhancement (LGE) imaging is a reference standard technique for the differentiation of ischemic cardiomyopathy (ICM) from non-ischemic dilated cardiomyopathy (NIDCM) in patients with heart failure and reduced ejection fraction (HFrEF). 3D myocardial deformation analysis (3D-MDA) offers highly reproducible phenotypic assessments of regional architecture and function that may provide value for artificial-intelligence-assisted cardiomyopathy diagnosis without need for LGE imaging. Purpose In this study, we trained and validated a machine-learning-based model to enable automated diagnosis of ischemic versus non-ischemic dilated cardiomyopathy exclusively using regional patterns of deformation among patients otherwise matched by age, sex and global contractile dysfunction. Methods 100 ICM and 100 NIDCM patients matched for age, sex, and LVEF underwent standard cine SSFP and LGE imaging. Patient diagnoses were established using a combination of clinical and LGE-based criteria. 3D-MDA was performed using validated software (GIUSEPPE) to compute regional 3D strain measures at each cardiac phase in both conventional and principal strain directions. Principal Component Analysis (PCA) was performed on the composite 3D-MDA dataset. The first 20 components were chosen, accounting for approximately 65% of the population variance. Subsequently, a support-vector-machine-based algorithm was used with 10-fold cross-validation to discriminate ICM from NIDCM. Results Patients were 63±10 years (ICM: 63±10 years, NIDCM: 63±10 years, p=0.955), 74% male (ICM: 74%, NIDCM: 74%, p=1.000), and had a mean LVEF of 27±8% (ICM: 27±7%, NIDCM: 28±7%, p=0.688). Global time to peak strain was significantly shorter in ICM patients relative to NIDCM patients across all surfaces and in all directions (p<0.05). The highest single-variable Area Under the Curve (AUC) achieved for the classification of ICM versus NIDCM from global data was for minimum principal strain (ICM: 43.7±7.8, NIDCM: 48.3±7.5, p<0.001, AUC: 0.682) (Figure 1). However, a multi-feature machine-learning-based model exposed to all available regional 3D deformation data achieved an AUC of 0.903 (sensitivity 87.7%, specificity 75.5%). Conclusions Machine learning-based analyses of3D regionaldeformation patterns allows for robust discrimination of ICM versus NIDCM. Further expansion of the presented findings is planned on a wider, multi-centre cohort. Acknowledgement/Funding Dr. White was supported by an award from Heart and Stroke Foundation of Alberta. This study was funded in part by Calgary Health Trust.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".