A deep learning model using echocardiography accurately detects cardiac amyloidosis, outperforming traditional methods.
Key Details
- 1AI model trained on echocardiography video clips from 2,612 patients across multiple sites and ethnic groups.
- 2External validation performed on 18 global sites; included 597 amyloidosis cases and 2,122 controls.
- 3Achieved AUROC of 0.93 (after excluding 13% uncertain predictions), sensitivity of 85%, specificity of 93%.
- 4Performance was consistent across amyloidosis subtypes and various subgroups.
- 5The AI model outperformed transthyretin cardiac amyloidosis (TTR-CA) score (AUROC = 0.73) and wall thickness scoring (AUROC = 0.8).
- 6Ultromics employees contributed and funded the study.
Why It Matters
This work demonstrates the robust, generalizable performance of AI to enhance echocardiographic detection of cardiac amyloidosis, surpassing current clinical scoring tools and suggesting real-world impact for earlier and more accurate diagnosis in routine practice.
Source
AuntMinnie
Related News
•Health Imaging
AI as Second Reader Surpasses Radiologists in Breast Cancer Screening
AI used as a second reader on mammograms improves cancer detection rates compared to radiologists alone.
•Health Imaging
AI-Powered Ultrasound Tool Predicts Delivery Timing for Pregnant Patients
Researchers have created an AI model using ultrasound to accurately forecast expectant mothers’ delivery timelines.
•AuntMinnie
ChatGPT-4 Turbo Powers Postdeployment Monitoring of ICH Detection AI
Researchers found ChatGPT-4 Turbo could efficiently monitor the performance of Aidoc's ICH detection AI across real-world radiology practices.