Researchers sought to show the incremental value of adding clinical and laboratory features to a transthoracic echocardiography-only AI model for cardiac amyloidosis (CA) diagnosis.The new model, dubbed AI-ECM, showed improved model performance in an internal validation study based on registry data.The ultimate goal is to improve early CA detection so patients can receive earlier treatment.
Researchers unveiled a new multimodal artificial intelligence (AI) algorithm for cardiac amyloidosis (CA) diagnosis, a tool showing promise for greater accuracy and sensitivity in the real world.
The AI echo-clinical model (AI-ECM) -- incorporating demographics, laboratory biomarkers, and transthoracic echocardiography (TTE) parameters -- performed better than the previously validated, FDA-cleared, TTE-only deep-learning model Us2.Ca, according to Federico Asch, MD, of MedStar Health Research Institute in Washington, D.C., and colleagues.
This was based on an internal validation study using multiethnic registry data, in which AI-ECM showed significant improvements in accuracy (area under the curve 0.94 vs 0.89) and sensitivity (93% vs 76%) compared with the older model. The one drawback was a drop in specificity (85% vs 91%), however.
