Decoding calcific mitral valve disease: a novel deep learning model uncovers the role of calcium burden
Bibliographic record
Abstract
Abstract Background Mitral annular calcification (MAC) is a common degenerative disease that affects older adults and causes calcium deposition in the mitral annulus, increasing the risk of mitral valve (MV) dysfunction, cardiovascular events, conduction abnormalities, and mortality. While the primary imaging modality recommended for the clinical evaluation of MAC is echocardiography, it has been reported to be found incidentally in approximately 8% of routine thoracic computed tomography (CT) scans. CT scan is a sensitive imaging modality for assessing MAC, providing the resolution necessary to understand the anatomical involvement of calcification. Research has shown that the MAC burden is associated with disease activity and progression. Still, the exact correlation between the quantitative calcium burden and the degree of severity of mitral valve disease is unclear. Purpose Our study aimed to determine the association between MAC, as assessed on CT, and significant mitral stenosis (MS) or mitral regurgitation (MR), as visualised on echocardiography. In addition, we sought to determine the quantitative MAC cutoffs that would optimally predict significant MS or MR on echocardiography. Methods A retrospective cohort study was conducted at our academic centre. Inclusion criteria were: age≥60 years, resting transthoracic echocardiogram performed between 2013-2022, non-gated chest CT performed within one year before echocardiography, and any degree of MAC documented on the echocardiography report. CT scans were requested for clinical indications unrelated to mitral valve disease. Exclusion criteria were: prior mitral valve intervention, endocarditis, and congenital mitral valve disease. Significant calcific mitral valve disease was defined as ≥moderate MR and ≥mild MS on the echocardiography report, ascertained by expert readers based on multiparametric ASE criteria. MAC volume was quantified on the multi-slice CT DICOM images using a 3D U-Net deep neural network previously trained by our group on an independent cohort. Results The cohort consisted of 1,560 unique patients with a mean age of 79 years and 61% females. The echocardiographic prevalence of significant MR and MS was 10% and 4%, respectively, for 211 affected patients. The CT-based mean MAC volume was 949 mm³ in patients with significant MR or MS, as opposed to 334 mm³ in those without (P<0.001). MAC volume >1000 mm³ was the optimal cutoff to predict significant MS (specificity 90%, sensitivity 51%, area under ROC curve 0.79) and significant MR (specificity 89%, sensitivity 21%, area under ROC curve 0.60). Adjusting for age, sex, and comorbid conditions did not affect the observed association between MAC volume and mitral valvulopathy. Conclusion A novel deep learning model for quantifying MAC volume from non-cardiac clinical CT scans was efficient in screening for calcific mitral valve disease, particularly MS, and identifying patients who may benefit from further evaluation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".