An AI model developed by Johns Hopkins significantly outperforms current risk scores in predicting post-surgical complications using routine ECG data.
Key Details
- 1Johns Hopkins researchers developed AI models to analyze pre-surgical electrocardiograms (ECG).
- 2Models were trained using data from 37,000 surgery patients at Beth Israel Deaconess Medical Center.
- 3The best-performing fusion model predicted serious post-surgical complications with 85% accuracy.
- 4Current clinical risk scoring methods are only about 60% accurate.
- 5The work was federally funded and results were published in the British Journal of Anaesthesia.
- 6The AI can identify previously undetectable signals in ECGs relevant to surgical risk.
Why It Matters
This work offers a major step forward in pre-surgical risk assessment, leveraging an inexpensive, routine test to more accurately identify patients at high risk of serious complications. It could directly impact patient management and surgical planning, highlighting the growing value of AI in medical imaging diagnostics.
Source
EurekAlert
Related News
•EurekAlert
New Daydreaming Algorithm Boosts Neural Networks' Recall of Biased Image Data
Researchers unveil a new 'Centered Daydreaming' algorithm enabling AI to effectively learn and recall from imbalanced, real-world image data.
•EurekAlert
Polymer-Based Flexible Wireless Sensors: AI-Driven Health Monitoring Review
A major review highlights how advanced polymer-based flexible wireless sensors, enhanced by AI-driven data processing, can transform continuous physiological health monitoring.
•EurekAlert
PET Imaging Uncovers Synaptic Loss Patterns in Schizophrenia
A major PET imaging study reveals widespread, non-random synaptic loss in schizophrenia, distinct from brain volume changes seen on MRI.