Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box.

ABSTRACT:

Aims

The spatiotemporal deep convolutional neural network (DCNN) helps reduce echocardiographic readers' erroneous 'judgement calls' on Takotsubo syndrome (TTS). The aim of this study was to improve the interpretability of the spatiotemporal DCNN to discover latent imaging features associated with causative TTS pathophysiology.

Methods and results

We applied gradient-weighted class activation mapping analysis to visualize an established spatiotemporal DCNN based on the echocardiographic videos to differentiate TTS (150 patients) from anterior wall ST-segment elevation myocardial infarction (STEMI, 150 patients). Forty-eight human expert readers interpreted the same echocardiographic videos and prioritized the regions of interest on myocardium for the differentiation. Based o

SUBMITTER: Zaman F 

PROVIDER: S-EPMC10944681 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature