Effectiveness of artificial intelligence in the diagnosis of congenital heart disease in pediatric population: a systematic review
DOI:
https://doi.org/10.24054/iss.v1i7.4473Keywords:
congenital heart diseases, artificial intelligence, machine learning, deep learning, diagnosis, echocardiography, systematic reviewAbstract
Congenital heart disease (CHD) presents a global clinical challenge because of its anatomical complexity and high operator dependence during diagnosis, leading to significant gaps in detection, especially in resource-limited settings. This systematic review aimed to evaluate the effectiveness of artificial intelligence (AI) in diagnosing CHD in the pediatric population. After analyzing 14 studies published between 2020 and 2025, encompassing modalities such as echocardiography, electrocardiography (ECG), phonocardiography, and radiography, a predominance of deep learning algorithms was observed. The results demonstrated a high overall diagnostic performance. Echocardiography stood out as the most effective modality, achieving accuracies of up to 100% and an AUC of 0.99, significantly exceeding the sensitivity of traditional community screening methods. Furthermore, automated ECG analysis has emerged as a powerful tool for mass triage. AI has matched or surpassed the performance of general practitioners in screening tasks, although direct comparisons with expert cardiologists remain limited. However, the evidence presents notable methodological limitations, such as a marked bias toward retrospective designs, a high geographic concentration in Asia, and a lack of standardization in the data reporting. In conclusion, AI has a high potential to transform screening, triage, and diagnostic support for coronary heart disease. However, its clinical translation urgently requires prospective multicenter validation, greater algorithmic transparency, and rigorous adoption of ethical and reporting guidelines (such as STARD-AI and TRIPOD+AI).
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