aceDeep 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 studie…
Pooled evidence from 160 studies suggests deep learning models reach high performance in echocardiography tasks and may reduce reliance on expert input in selected tasks.
Unresolved high heterogeneity in chamber segmentation and view classification studies, plus a lack of real-world validation, limits confidence in clinical adoption.
Heterogeneity across segmentation and view classification studies could not be resolved, and the authors state that further validation, clinical utility assessment, and real-world evaluation are needed before widespread adoption.
Evidence
- Peer-reviewedEuropean Heart Journal - Digital Health2026-10-19
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Truvace Impact Record TRV-2026-2049, v1: “Deep learning-based echocardiography for cardiac assessment: a systematic literature review and meta-analysis.” Truvace, 2026-10-20. /record/TRV-2026-2049 (accessed at citation time). sha256 9f1b0fb264ec55ae…
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