Subclinical Atrial Fibrillation Prediction in Patients with CIED by a Novel Deep Learning Framework.

ABSTRACT:

Background

Subclinical atrial fibrillation (SCAF), a key risk factor for cryptogenic stroke, is difficult to predict with current tools. This study aimed to develop a novel deep learning framework, ResKAN-Attention, using only routine clinical data to predict SCAF in patients with cardiac implantable electronic device (CIED).

Methods

In this retrospective study, the ResKAN-Attention model was developed using 27 routine parameters from 124 CIED patients without prior AF. This framework features a dual-path architecture combining a Kolmogorov-Arnold Network (KAN) with a traditional multilayer perceptron, fused via a cross-attention mechanism. The model's performance was evaluated against common baselines using five-fold cross-validation, while its decision-making process was a

SUBMITTER: Lan Y 

PROVIDER: S-EPMC12842029 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature