
AI connects gum disease to heart attacks
A new machine learning model proves that dental records might be just as important as cholesterol levels for predicting heart attacks.
Discover the newest research about AI innovations in 🫀 Cardiology.

A new machine learning model proves that dental records might be just as important as cholesterol levels for predicting heart attacks.

By blending cardiovascular physics into machine learning, researchers have cut the data needed to track continuous blood pressure in half.

By fusing electrical and structural data, a new multimodal model flags hidden heart valve risks before symptoms appear.

A standard ten-second heart trace holds hidden data that could predict when a patient will die, but clinics are not equipped to read it.

A new deep learning model proves that combining visual breathing patterns with heart scans can predict which emergency patients will need a hospital bed.

A new model bypasses rigid labels to turn raw cardiac waveforms directly into human-readable clinical narratives.

A new AI model extracts metabolic risk data directly from routine heart ultrasounds, bypassing the need for expensive CT scans.

A new composite AI model can spot structural heart disease from a standard ECG before symptoms even appear, but its real value lies in predicting future risk.

A new machine learning model proves that how your blood pressure fluctuates over 24 hours is far more dangerous than a single high reading at the clinic.

Point-of-care cardiac assessment is moving from subjective clinical guesswork to automated, real-time quantification.