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

TL;DR

This document describes the elsarticle.cls LaTeX class, a rewritten formatting framework for Elsevier journal submissions. It emphasizes compatibility with the LaTeX kernel and common packages, providing robust integration with natbib for citations and flexible formatting options to support various journal models. It contrasts elsarticle.cls with the older elsart.cls, highlighting improved compatibility, diverse output formats, and streamlined theorem-like environments. Installation guidance covers obtaining the class from Elsevier or CTAN, generating the.cls file from source, placing it in the TEXMF tree, and configuring a range of options (default, preprint, single- and multi-column final formats) for precise submission and publication-ready layouts. The guidance also notes support for frontmatter elements like abstracts and keywords, and standard handling of figures and tables.

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.