Deep Learning Model of Diastolic Dysfunction Risk Stratifies the Progression of Early-Stage Aortic Stenosis
Bibliographic record
Abstract
ABSTRACT Background The development and progression of aortic stenosis (AS) from aortic valve (AV) sclerosis is highly variable and difficult to predict. Objectives We investigated whether a previously validated echocardiography-based deep learning (DL) model assessing diastolic dysfunction (DD) could identify the latent risk associated with the development and progression of AS. Methods We evaluated 898 participants with AV sclerosis from the Atherosclerosis Risk in Communities (ARIC) cohort study and associated the DL-predicted probability of DD with two endpoints: (1) the new diagnosis of AS and (2) the composite of subsequent mortality or AV interventions. We performed validation in two additional cohorts: 1) patients with mild-to-moderate AS undergoing cardiac magnetic resonance (CMR) imaging and serial echocardiographic assessments (n=50), and (2) patients with AV sclerosis undergoing 18 F-sodium fluoride ( 18 F-NaF) and 18 F-fluorodeoxyglucose positron emission tomography (PET) combined with computed tomography (CT) to assess valvular inflammation and calcification (n=18). Results In the ARIC cohort, a higher DL-predicted probability of DD was associated with the development of AS (adjusted HR: 3.482 [2.061 – 5.884], p<0.001) and subsequent mortality or AV interventions (adjusted HR: 7.033 [3.036 – 16.290], p<0.001). The multivariable Cox model (incorporating the DL-predicted probability of DD) derived from the ARIC cohort efficiently predicted the progression of AS (C-index: 0.798 [0.648 – 0.948]) in the CMR cohort. Moreover, the predictions of this multivariable Cox model correlated positively with valvular 18 F-NaF mean standardized uptake values in the PET/CT cohort (r=0.62, p=0.008). Conclusions Assessment of DD using DL can stratify the latent risk associated with the progression of early-stage AS. CONDENSED ABSTRACT We investigated whether DD assessed using DL can predict the progression of early-stage AS. In 898 patients with AV sclerosis, the DL-predicted probability of DD was associated with the development of AS. The multivariable Cox model derived from these patients also predicted the progression of AS in an external cohort of patients with mild-to-moderate AS (n=50). Moreover, the predictions of this model correlated positively with PET/CT-derived valvular 18 F-NaF uptake in an additional cohort of patients with AV sclerosis (n=18). These findings suggest that assessing DD using DL can stratify the latent risk associated with the progression of early-stage AS.
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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 | 0.003 |
| 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 | 0.001 |
| 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".