Artificial Intelligence in Diagnosis of Long QT Syndrome: A Review of Current State, Challenges, and Future Perspectives

• Published in: Mayo Clinic Proceedings. Digital health Vol. 2; no. 1; pp. 21 - 31
• Main Authors: Raissi Dehkordi, Negar, Raissi Dehkordi, Nastaran, Karimi Toudeshki, Kimia, Farjoo, Mohammad Hadi
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.03.2024; Elsevier
• Subjects: Review; FCN; QTc; TdP; LQTS; AI; DL; ECG; mECG; CNNs; ML
• ISSN: 2949-7612; 2949-7612
• DOI: 10.1016/j.mcpdig.2023.11.003

Long QT syndrome (LQTS) is a potentially life-threatening cardiac repolarization disorder characterized by an increased risk of fatal arrhythmias. Accurate and timely diagnosis is essential for risk stratification and appropriate management. However, traditional diagnostic approaches have limitations, necessitating more objective and efficient tools. Artificial intelligence (AI) offers promising solutions by enhancing the accuracy and efficiency of electrocardiography (ECG) interpretation. The AI algorithms can process ECG data more rapidly than human experts, providing real-time analysis and prompt identification of individuals at risk, and reducing interobserver variability. By analyzing large volumes of ECG data, AI algorithms can extract meaningful features that may not be apparent to the human eye. Advancements in AI-driven corrected QT interval monitoring using mobile ECG devices, such as smartwatches, offer a valuable and convenient tool for identifying individuals at risk of LQTS-related complications, which is particularly applicable during pandemic conditions, such as COVID-19. Integration of AI into clinical practice poses a number of challenges. Bias in data gathering and patient privacy concerns are important considerations that must be addressed. Safeguarding patient privacy and ensuring data protection are crucial for maintaining trust in AI-driven systems. In addition, the interpretability of AI algorithms is a concern because understanding the decision-making process is essential for clinicians to trust and confidently use these tools. Future perspectives in this field may involve the integration of AI into diagnostic protocols, through genetic subtype classifications on the basis of ECG data. Moreover, explainable AI techniques aim to uncover ECG features associated with LQTS diagnosis, suggesting new insights into LQTS pathophysiology.