Key result
AI shows potential to automate image acquisition, segmentation, and CHD detection in fetal and pediatric echocardiography.
Why the study?
Fetal and pediatric echocardiography faces limitations including operator variability, complex interpretation, and workforce shortages, which artificial intelligence could potentially address.
AI has promising applications in automating and improving fetal and pediatric echocardiography, but requires overcoming data and implementation challenges.
May support AI automation in fetal echocardiography; leaves open validation and data challenges before clinical adoption.
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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Wang et al. (2024) conducted a review in acquired and congenital heart disease (CHD). Artificial intelligence (AI) was evaluated. Artificial intelligence offers significant potential to automate image acquisition, segmentation, and congenital heart disease detection in fetal and pediatric echocardiography.
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