TRAITER: transformer-guided diagnosis and prognosis of heart failure using cell nuclear morphology and DNA damage marker.

Abstract

Motivation: Heart failure (HF), a major cause of morbidity and mortality, necessitates precise diagnostic and prognostic methods.

Results: This study presents a novel deep learning approach, Transformer-based Analysis of Images of Tissue for Effective Remedy (TRAITER), for HF diagnosis and prognosis. Using image segmentation techniques and a Vision Transformer, TRAITER predicts HF likelihood from cardiac tissue cell nuclear morphology images and the potential for left ventricular reverse remodeling (LVRR) from dual-stained images with cell nuclei and DNA damage markers. In HF prediction using 31 158 images from 9 patients, TRAITER achieved 83.1% accuracy. For LVRR prediction with 231 840 images from 46 patients, TRAITER attained 84.2% accuracy for individual images and 92.9% for individual patients. TRAITER outperformed other neural network models in terms of receiver operating characteristics, and precision-recall curves. Our method promises to advance personalized HF medicine decision-making.

Availability and implementation: The source code and data are available at the following link: https://github.com/HamanoLaboratory/predict-of-HF-and-LVRR.