Abstract 16082: Machine Learning to Improve Left Ventricular Scar Quantification in Hypertrophic Cardiomyopathy Patients
Bibliographic record
Abstract
Introduction: Accurate and reproducible scar quantification of late gadolinium enhancement (LGE) images from cardiac magnetic resonance imaging (CMR) is important in risk stratifying hypertrophic cardiomyopathy (HCM) patients. Previous machine learning algorithms for CMR LGE quantification deployed three-dimensional convolutional neural network (CNN) architecture, which required image cropping and custom graphic processing units (GPUs) to function, thus limiting their general applicability. We aim to develop a deep two-dimensional (2D) CNN model that contours the left ventricle (LV) endo- and epicardial borders and quantifies LGE. Hypothesis: We hypothesize that a deep 2D CNN model, which uses commercially available GPUs, could be used to efficiently and accurately contour LV endo- and epicardial borders and quantify CMR LGE in HCM patients. Methods: We retrospectively studied 296 HCM patients (2423 images) from the University Health Network (Toronto, Canada) and Tufts Medical Center (Boston, USA). LGE images were manually segmented by an expert reader. Scar was defined as 5 standard deviations higher than the mean of the annotated normal region pixels. A 2D U-net CNN variant was used to train a model on 80% of the datasets. Testing was performed on the remaining 20%. We applied a 5-folds cross validation algorithm for training to improve model robustness. Model performance was assessed using the Dice Similarity Coefficient (DSC). Results: We were able to develop a deep learning model that could successfully perform both LV segmentation and scar quantification using a generally available GPU card. Our algorithm did not require image cropping and processed one image every 60 milliseconds. DSC scores averaged across the 5-folds was excellent at 0.89+0.22 for the endocardium and 0.81+0.17 for the epicardium, and good at 0.57+0.31 for scar. Conclusions: Using novel 2D CNN methods, we have successfully developed an automatic algorithm that rapidly provides LV endo- and epicardial contours and scar quantification on LGE CMR images that is superior to previously published studies. Unlike previous algorithms, our program does not require the use of custom CPUs or image cropping, potentially allowing it to be integrated into routine clinical practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".