Current and future use of artificial intelligence in valvular heart disease imaging
Bibliographic record
Abstract
Valvular heart disease (VHD) remains significantly underdiagnosed and undertreated. This review examines an artificial intelligence (AI)-enhanced 'spoke-hub-node' care model designed to improve the early detection, risk stratification, and treatment of VHD. In this model, AI tools-such as automated ECG interpretation, digital stethoscopes, and point-of-care ultrasound-facilitate decentralized screening and referral for cardiac imaging at the community level. During the transition from outpatient settings to tertiary care centres, AI-integrated echocardiography, cardiac tomography, and magnetic resonance imaging facilitate advanced diagnostic evaluation and inform procedural planning. We review emerging innovations that can enhance this model of care delivery-including unsupervised machine learning to uncover novel VHD phenotypes, generative AI for automated reporting, the use of digital twins to simulate interventions, and the integration of multiple AI agents to support heart team meetings. These advances are followed by the emerging use of AI in robotic transoesophageal and intracardiac echocardiography, as well as in fusion fluoroscopy imaging, to guide valve interventions. While outlining the challenges inherent in this rapidly evolving field, the review's central contribution is its vision to connect the continuum-from AI-enabled community screening to personalized, image-guided therapies at tertiary care centres-offering a scalable and equitable model for VHD care.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".