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Updated: May 31, 2025
Murine Fetal Echocardiography
Published on: February 15, 2013
Artificial Intelligence in Fetal and Pediatric Echocardiography
Alan Wang1, Tam T Doan2, Charitha Reddy3
1Division of Pediatric Cardiology, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, USA.
Insights
Artificial intelligence (AI) can improve fetal and pediatric echocardiography by automating tasks and aiding in congenital heart disease (CHD) detection. Further research is needed to address data limitations and ensure AI integration into clinical practice.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Echocardiography is crucial for diagnosing fetal and pediatric heart conditions.
- Limitations include operator variability and interpretation complexity.
- Shortages of expert sonographers and cardiologists exist in some regions.
Purpose of the Study:
- To review current applications of AI in fetal and pediatric echocardiography.
- To identify challenges hindering AI adoption.
- To discuss future directions for AI integration.
Main Methods:
- Review of existing literature on AI in echocardiography.
- Analysis of AI's role in image acquisition, segmentation, and disease detection.
- Discussion of challenges and future prospects.
Main Results:
- AI, including machine learning and deep learning, shows potential to automate echocardiography tasks.
- AI can assist in congenital heart disease detection and measurements.
- Current challenges include limited datasets and algorithm transparency.
Conclusions:
- AI offers significant potential to enhance fetal and pediatric echocardiography.
- Addressing challenges like data availability and physician trust is essential for widespread AI integration.
- Future work should focus on overcoming these barriers for clinical implementation.
Abstract:
Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. Despite these promising advancements, challenges such as small number of datasets, algorithm transparency, physician comfort with AI, and accessibility must be addressed to fully integrate AI into practice. This review highlights AI's current applications, challenges, and future directions in fetal and pediatric echocardiography.
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