Deep learning-based echocardiography for cardiac assessment: a systematic literature review and meta-analysis.

Aims: Artificial intelligence, particularly deep learning (DL), has shown significant potential for enhancing the assessment of echocardiograms in clinical practice. This systematic review and meta-analysis aimed to evaluate the performance of DL models and to identify factors affecting their performance. Methods and results: A systematic review of the PubMed, CENTRAL, and Web of Science databases was conducted, yielding 16 248 potential publications. Of these, 160 studies met the inclusion criteria. Only studies applying fully automated DL networks to echocardiographic loops were considered. Performance estimates were pooled using a random-effects meta-analysis. The studies were categorized into five distinct groups: prediction of disease or mortality, replication of established measures, segmentation of cardiac chambers, classification of chamber views, and assessment of image quality. High heterogeneity that could not be resolved was observed in studies focusing on cardiac chamber segmentation and classification. Model performance was influenced by a variety of different factors, including medical target and input characteristics such as video resolution, number of frames used, and total participants included during training. Conclusion: DL models achieve high performance across various applications in echocardiography. In selected tasks, they may reduce reliance on expert input and support greater automation of echocardiographic workflows. However, further validation, assessment of clinical utility, and evaluation in real-world settings are required before widespread clinical adoption.