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TRUVACE RECORD VERSION
record: TRV-2026-2049
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-20T22:03:33.216535Z
status: published
lens: trace
sector: health
headline: Deep learning-based echocardiography for cardiac assessment: a systematic literature review and meta-analysis
dek: 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…
gain_title: 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.
problem_title: Unresolved high heterogeneity in chamber segmentation and view classification studies, plus a lack of real-world validation, limits confidence in clinical adoption.
trace_subject: Deep learning models applied to echocardiographic loops for cardiac assessment
gain_reading: 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.
gain_evidence: DL models achieve high performance across various applications in echocardiography. | they may reduce reliance on expert input and support greater automation of echocardiographic workflows
problem_reading: Unresolved high heterogeneity in chamber segmentation and view classification studies, plus a lack of real-world validation, limits confidence in clinical adoption.
problem_evidence: High heterogeneity that could not be resolved was observed in studies focusing on cardiac chamber segmentation and classification. | further validation, assessment of clinical utility, and evaluation in real-world settings are required before widespread clinical adoption
quick_read: Researchers searched three databases, screened 16,248 potential publications, and included 160 studies that applied fully automated deep learning networks to echocardiographic loops. They pooled performance with random-effects meta-analysis across five task groups: disease or mortality prediction, replicating established measures, chamber segmentation, view classification, and image quality assessment.

The findings suggest automation could ease some expert-dependent steps in echocardiography, but the evidence is not yet ready to support routine use. Unresolved heterogeneity in some task groups and the call for clinical utility and real-world evaluation leave open how well the models will work in everyday practice.
limitation: 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.
tag: Dual reading
key_points: The review searched PubMed, CENTRAL, and Web of Science, yielding 16,248 potential publications. | 160 studies met the inclusion criteria, which covered only fully automated deep learning networks applied to echocardiographic loops. | Tasks were grouped into prediction of disease or mortality, replication of established measures, chamber segmentation, view classification, and image quality assessment. | Performance varied with the medical target, video resolution, number of frames used, and training participant numbers.
rundown: The authors pooled performance estimates with a random-effects meta-analysis, a method that allows for differences between studies. They sorted the studies into five groups by task rather than treating all echocardiography models as a single category.

Two of those groups, chamber segmentation and chamber view classification, showed high heterogeneity that the analysis could not resolve. This means pooled results for those tasks should be read with caution.

The authors also identified factors that influenced model performance, including the medical target, video resolution, number of frames used, and the total number of participants included during training. They conclude that models may support automation in selected tasks, but that further validation, assessment of clinical utility, and real-world evaluation are needed first.
sources:
- peer_reviewed | European Heart Journal - Digital Health | https://doi.org/10.1093/ehjdh/ztag148 | 2026-10-19
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