Artificial intelligence for left ventricular hypertrophy detection and differentiation on echocardiography, cardiac magnetic resonance and cardiac computed tomography: A systematic review

Aims: Left ventricular hypertrophy (LVH) is a common clinical finding associated with adverse cardiovascular outcomes. Once LVH is diagnosed, defining its cause has crucial clinical implications. Artificial intelligence (AI) may allow significant progress in the automated detection of LVH and its underlying causes from cardiovascular imaging. This systematic review aims to investigate the diagnostic performance of AI models developed to diagnose LVH and its common aetiologies. Methods: MEDLINE/PubMed, EMBASE and Cochrane databases were systematically searched to identify relevant studies on echocardiography, cardiac magnetic resonance (CMR), and cardiac computed tomography (CT). Results: Thirty studies were included in this review. Of them, 14 were on echocardiography, 15 on CMR, and one on cardiac CT. Regarding the AI methods applied, 79 % of studies in echocardiography utilized deep learning (DL), 64 % employed convolutional neural networks (CNNs), and 21 % applied traditional machine learning (ML) algorithms. For CMR studies, 53 % used DL, 27 % relied on CNNs, and 47 % adopted traditional ML methods. All studies showed good diagnostic performances, but those applying AI tools to determine the underlying causes of LVH demonstrated the highest accuracy metrics compared to those focused on detecting LVH itself. Conclusion: AI models designed to detect and differentiate LVH on cardiac imaging are currently under development and are demonstrating promising results. Further studies focusing on real-life validation of these models, and cost-effectiveness analyses are needed.