An AI tool using echocardiograms accurately screens for cardiac amyloidosis, outperforming traditional detection methods.
Key Details
- 1AI model analyzes echocardiogram (ultrasound) images to screen for cardiac amyloidosis.
- 2Developed by Mayo Clinic and Ultromics; validated in a global, multi-ethnic cohort across 18 hospitals.
- 3Achieved 85% sensitivity and 93% specificity in detecting or ruling out cardiac amyloidosis.
- 4Outperformed existing clinical scoring systems for early diagnosis.
- 5FDA-cleared and already being implemented in multiple US hospitals.
Why It Matters
Early, accurate identification of cardiac amyloidosis is critical due to effective therapies available only at earlier disease stages. AI-based screening of routine cardiac ultrasound could substantially improve detection rates and patient outcomes, supporting broader integration of imaging AI in clinical care.
Source
EurekAlert
Related News
•EurekAlert
UVA's $4M NIH Grant Backs AI to Optimize Cardiac MRI Therapies
UVA secures $4 million NIH funding to develop GenStrain, an AI model using MRI data to personalize cardiac pacing therapies.
•EurekAlert
Machine Learning Models Predict Cancer Risk with Routine Health Data
Researchers developed machine learning models using routinely collected health data to predict individual risk for 20 common cancers, outperforming traditional methods for several cancer types.
•EurekAlert
AI Deep Learning Model Enhances Echocardiogram RV Assessment and Prognosis
Researchers developed DROID-RV, a deep learning tool that automates right ventricular assessment from echocardiograms, showing strong prognostic links with cardiovascular outcomes.