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
•AuntMinnie
Deep Learning AI Outperforms Radiologists in Detecting ENE on CT
A deep learning tool, DeepENE, exceeded radiologist performance in identifying lymph node extranodal extension in head and neck cancers using preoperative CT scans.
•Radiology Business
Patients Favor AI in Imaging Diagnostics, Hesitate on Triage Use
Survey finds most patients support AI in diagnostic imaging but are reluctant about its use in triage decisions.
•Radiology Business
FDA Clears Multi-Disease AI Screening Platform for CT Imaging
HeartLung Corporation's AI-CVD platform receives FDA clearance to detect multiple diseases from a single CT scan.