Cardiac amyloidosis detection from a single echocardiographic video clip: a novel artificial intelligence-based screening tool.

ABSTRACT:

Background and aims

Accurate differentiation of cardiac amyloidosis (CA) from phenotypic mimics remains challenging using current clinical and echocardiographic techniques. The accuracy of a novel artificial intelligence (AI) screening algorithm for echocardiography-based CA detection was assessed.

Methods

Utilizing a multisite, multiethnic dataset (n = 2612, 52% CA), a convolutional neural network was trained to differentiate CA from phenotypic controls using transthoracic apical four-chamber video clips. External validation was conducted globally across 18 sites including 597 CA cases and 2122 controls. Classification accuracy was assessed on the entire external validation dataset, and subgroup analyses were performed both on technetium pyrophosphate scintigraphy referrals

SUBMITTER: Slivnick JA 

PROVIDER: S-EPMC12539910 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature