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Implantation of Total Artificial Heart in Congenital Heart Disease
Published on: July 18, 2014
Accurately assessing congenital heart disease using artificial intelligence
Khalil Khan1, Farhan Ullah2, Ikram Syed3
1Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
Insights
Machine learning (ML) models offer improved accuracy in predicting congenital heart disease (CHD) mortality risk. This review analyzes ML methods, datasets, and future directions for enhanced CHD diagnosis and care.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Cardiology
Background:
- Congenital heart disease (CHD) is a major cause of newborn mortality, especially in low-resource settings.
- Limited healthcare resources exacerbate the impact of CHD globally.
- Accurate risk assessment and early diagnosis are critical for improving infant outcomes.
Purpose of the Study:
- To provide a comprehensive analysis of machine learning (ML) methods for CHD diagnosis over the past eight years.
- To describe available datasets for CHD research and their relevance to ML applications.
- To critically evaluate existing ML algorithms, their strengths, weaknesses, and limitations in CHD identification.
Main Methods:
- Systematic review and analysis of ML techniques applied to CHD diagnosis.
- Evaluation of various datasets used in CHD research, including data collection and characteristics.
- Critical assessment of the performance and limitations of current ML algorithms for CHD.
Main Results:
- ML models show significant potential in accurately assessing CHD mortality risk.
- ML algorithms enhance diagnostic accuracy by identifying complex patterns often missed by clinicians.
- A review of ML methods, datasets, and algorithms provides insights into current capabilities and challenges.
Conclusions:
- ML offers a promising avenue for improving the identification and management of CHD.
- Further research is needed to fully leverage ML's potential in CHD diagnosis and treatment.
- Future directions focus on enhancing the efficacy of ML for better infant outcomes in CHD cases.
Abstract:
Congenital heart disease (CHD) remains a significant global health challenge, particularly contributing to newborn mortality, with the highest rates observed in middle- and low-income countries due to limited healthcare resources. Machine learning (ML) presents a promising solution by developing predictive models that more accurately assess the risk of mortality associated with CHD. These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care. In addition, ML algorithms excel at detecting and analyzing complex patterns that can be overlooked by human clinicians, thereby enhancing diagnostic accuracy. Despite notable advancements, ongoing research continues to explore the full potential of ML in the identification of CHD. The proposed article provides a comprehensive analysis of the ML methods for the diagnosis of CHD in the last eight years. The study also describes different data sets available for CHD research, discussing their characteristics, collection methods, and relevance to ML applications. In addition, the article also evaluates the strengths and weaknesses of existing algorithms, offering a critical review of their performance and limitations. Finally, the article proposes several promising directions for future research, with the aim of further improving the efficacy of ML in the diagnosis and treatment of CHD.
