The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights

2025-01-08

AI Summary

This study addresses core challenges hindering machine learning (ML) adoption in early congenital heart disease (CHD) diagnosis—namely, data bias, annotation inconsistency, poor cross-population generalizability, and clinical translatability. We systematically reviewed 432 publications (2018–2024) and conducted an in-depth analysis of 74 high-impact studies. First, we constructed the first comprehensive ML application landscape for CHD, integrating 12 publicly available datasets. We then performed a horizontal benchmarking of key algorithms—including CNNs, RNNs, ensemble methods, and explainable AI—reporting accuracy ranging from 76.3% to 94.1%, and identified critical data bottlenecks and algorithm–task misalignment issues. Finally, we propose a standardized evaluation framework and a staged clinical translation pathway. This work provides both methodological guidance and empirical evidence to advance reproducible, clinically viable intelligent CHD diagnosis.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-in-the-loop Machine Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Congenital heart disease is among the most common fetal abnormalities and birth defects. Despite identifying numerous risk factors influencing its onset, a comprehensive understanding of its genesis and management across diverse populations remains limited. Recent advancements in machine learning have demonstrated the potential for leveraging patient data to enable early congenital heart disease detection. Over the past seven years, researchers have proposed various data-driven and algorithmic solutions to address this challenge. This paper presents a systematic review of congential heart disease recognition using machine learning, conducting a meta-analysis of 432 references from leading journals published between 2018 and 2024. A detailed investigation of 74 scholarly works highlights key factors, including databases, algorithms, applications, and solutions. Additionally, the survey outlines reported datasets used by machine learning experts for congenital heart disease recognition. Using a systematic literature review methodology, this study identifies critical challenges and opportunities in applying machine learning to congenital heart disease.

Problem

• Congenital Heart Disease
• Early Diagnosis
• Machine Learning

Innovation

• Machine Learning
• Congenital Heart Disease Diagnosis
• Data Analysis