Emerging Visual Language Models in Analysis of Echocardiography, Can They Solve the Challenges of Complex Congenital Heart Disease Echocardiography?

Abstract

Echocardiography is vital in diagnosing and managing congenital heart disease (CHD), particularly in the pediatric population, necessitating detailed structural and functional assessments. Artificial intelligence (AI) has revolutionized echocardiographic analysis, particularly in functional assessments and the detection of valvular lesions. While convolutional neural networks (CNNs) dominate image-based tasks, emerging vision language models (VLMs) are transforming report generation by integrating multimodal data. This review explores the current state of AI in echocardiography, emphasizing the potential of VLMs to provide comprehensive reports and image-specific diagnoses. Despite significant advancements, several challenges hinder the development of holistic AI software for diagnosing complex CHD (CXCHD). These challenges include the heterogeneity of CHD, limited access to high-quality labeled datasets, variability in imaging techniques, and the need for expertise in image annotation. This review highlights the necessity for robust algorithms, standardized protocols, and diverse training datasets to fully realize the potential of AI in CXCHD diagnosis.

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