Artificial Intelligence-Based Three-Dimensional Cardiovascular CT Segmentation and Quantification for Congenital Heart Disease: What We Have Learned

Abstract

Artificial intelligence-based segmentation and quantification is a hot topic in cardiovascular imaging.However, the development of clinically useful artificial intelligence-based models for congenital heart disease is challenging owing to morphologic heterogeneity and limited data for training.Although cardiovascular computed tomography is increasingly used in evaluating patients with congenital heart disease, only a few reports have evaluated artificial intelligence-based segmentation algorithms for congenital heart disease.To be familiarized with this trend, we need to understand state-of-the-art segmentation techniques for cardiovascular digital twins as well as basic concept, current challenges, and potential solutions of artificial intelligence-based methods.Additionally, in this review, our learnings from the literature and our pilot study are summarized, and future directions are suggested.

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