UC San Francisco researchers developed a multiview deep learning network that improves diagnostic accuracy for major cardiac conditions from echocardiography data.
Key Details
- 1Researchers from UCSF created a 'multiview' deep neural network (DNN) architecture for echocardiograms.
- 2The model integrates data from multiple imaging views, instead of using a single 2D view.
- 3The DNNs were trained and tested on disease detection tasks for left/right ventricular abnormalities, diastolic dysfunction, and valvular regurgitation.
- 4Performance was superior to single-view DNNs, demonstrating improved diagnostic accuracy on real-world echo datasets.
- 5Findings are published in Nature Cardiovascular Research (March 17, 2026; DOI: 10.1038/s44161-026-00786-7).
- 6The research was funded by the National Institutes of Health.
Why It Matters
This approach addresses a long-standing limitation in imaging AI by combining information across multiple views, which better reflects clinical echocardiography practice. Improved model accuracy in echo interpretation could advance automated cardiac diagnostics and be translatable to other multi-view medical imaging modalities.
Source
EurekAlert
Related News
•EurekAlert
FDA Approves Johns Hopkins AI Tool for Early Sepsis Detection
FDA clears an AI-driven system developed by Johns Hopkins to detect sepsis up to 48 hours earlier and reduce mortality rates.
•EurekAlert
New AI Vision-Language Model Enhances Chest CT Diagnostics
Researchers developed an interpretable AI model that uses visual question answering to generate detailed diagnostic findings from chest CT scans, aimed at improving lung cancer diagnosis.
•EurekAlert
Optical AI Chip Boosts Real-Time Dry Eye Gland Diagnosis Accuracy
A new metasurface spectral AI chip enables rapid, accurate diagnosis of meibomian gland dysfunction (MGD) from tissue samples, achieving 96.22% accuracy.