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
AI Model Accurately Predicts Blood Loss Risk in Liposuction
A machine learning model predicts blood loss during high-volume liposuction with 94% accuracy.

•EurekAlert
AI-Driven CT Tool Predicts Cancer Spread in Oropharyngeal Tumors
Researchers have created an AI tool that uses CT imaging to predict the spread risk of oropharyngeal cancer, offering improved treatment stratification.

•EurekAlert
AI Model PRTS Predicts Spatial Transcriptomics From H&E Histology Images
Researchers developed PRTS, a deep learning model that infers single-cell spatial transcriptomics from standard H&E-stained tissue images.