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. 2025 Apr 27;26(4):28060.
doi: 10.31083/RCM28060. eCollection 2025 Apr.

Artificial Intelligence-Assisted Echocardiographic Image-Analysis for the Diagnosis of Fetal Congenital Heart Disease: A Systematic Review and Meta-Analysis

Yaduan Gan  1 , Lin Yang  1 , Jianmei Liao  1
Affiliations

Artificial Intelligence-Assisted Echocardiographic Image-Analysis for the Diagnosis of Fetal Congenital Heart Disease: A Systematic Review and Meta-Analysis

Yaduan Gan et al. Rev Cardiovasc Med. .

Abstract

Background: To assess the precision of artificial intelligence (AI) in aiding the diagnostic process of congenital heart disease (CHD).

Methods: PubMed, Embase, Cochrane, and Web of Science databases were searched for clinical studies published in English up to March 2024. Studies using AI-assisted ultrasound for diagnosing CHD were included. To evaluate the quality of the studies included in the analysis, the Quality Assessment Tool for Diagnostic Accuracy Studies-2 scale was employed. The overall accuracy of AI-assisted imaging in the diagnosis of CHD was determined using Stata15.0 software. Subgroup analyses were conducted based on region and model architecture.

Results: The analysis encompassed a total of 7 studies, yielding 19 datasets. The combined sensitivity was 0.93 (95% confidence interval (CI): 0.88-0.96), and the specificity was 0.93 (95% CI: 0.88-0.96). The positive likelihood ratio was calculated as 13.0 (95% CI: 7.7-21.9), and the negative likelihood ratio was 0.08 (95% CI: 0.04-0.13). The diagnostic odds ratio was 171 (95% CI: 62-472). The summary receiver operating characteristic (SROC) curve analysis revealed an area under the curve of 0.98 (95% CI: 0.96-0.99). Subgroup analysis found that the ResNet and DenNet architecture models had better diagnostic performance than other models.

Conclusions: AI demonstrates considerable value in aiding the diagnostic process of CHD. However, further prospective studies are required to establish its utility in real-world clinical practice.

The prospero registration: CRD42024540525, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=540525.

Keywords: artificial intelligence; congenital heart disease; diagnostic accuracy; fetal echocardiography; meta-analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
The flowchart of the literature screening process.
Fig. 2.
Fig. 2.
Quality assessment using quality assessment of diagnostic accuracy studies (QUADAS-2) for included studies.
Fig. 3.
Fig. 3.
Forest plot of combined sensitivity and specificity for the assessment of artificial intelligence (AI)-assisted diagnosis of congenital heart disease (CHD). CI, confidence interval.
Fig. 4.
Fig. 4.
Summary receiver operating characteristic curve of AI-assisted diagnosis of CHD. SENS, sensitivity; SPEC, specificity; SROC, summary receiver operating characteristic; AUC, area under the curve; AI, artificial intelligence; CHD, congenital heart disease.
See this image and copyright information in PMC

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