# The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging

## TL;DR

This paper reviews how artificial intelligence is being used in heart imaging to assess valvular heart disease, highlighting its potential benefits and challenges.

## Contribution

A comprehensive overview of AI applications in multimodality cardiac imaging for valvular heart disease assessment.

## Key findings

- AI models are most advanced in transthoracic echocardiography for whole-study interpretation.
- AI applications in CCT and CMR are promising but still in early development stages.
- Challenges include generalizability, transparency, and integrating AI into clinical workflows.

## Abstract

This review summarizes current applications of artificial intelligence (AI) in multimodality cardiac imaging for the evaluation of valvular heart disease (VHD).

The prevalence of VHD continues to rise, placing increasing demands on cardiovascular imaging and longitudinal management. AI systems have been applied across echocardiography, cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR) to automate image classification, segmentation, disease detection, and severity assessment. The most mature AI models have centered on transthoracic echocardiography (TTE), where deep learning (DL) frameworks enable whole-study interpretation and preliminary report generation. Applications in CCT and CMR remain in earlier stages but show promise for segmentation, tissue characterization, and pre-procedural planning.

AI has the potential to enhance the accuracy, reproducibility, and efficiency of imaging-based VHD assessment. Key challenges remain around generalizability, transparency, and clinical integration. Multidisciplinary collaboration is essential to ensure that AI complements, rather than replaces, human expertise.