Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram-Applicable in Clinical Practice?-Critical Literature Review with Meta-Analysis

Abstract

Background/Objectives: The increasing utilization of artificial intelligence (AI) in the medical field holds the potential to address the global shortage of doctors. However, various challenges, such as usability, privacy, inequality, and misdiagnosis, complicate its application. This literature review focuses on AI's role in cardiology, specifically its impact on the diagnostic accuracy of AI algorithms analyzing 12-lead electrocardiograms (ECGs) to detect left ventricular hypertrophy (LVH). Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of PubMed, CENTRAL, Google Scholar, Web of Science, and Cochrane Library. Eligible studies included randomized controlled trials (RCTs), observational studies, and case-control studies across various settings. This review is registered in the PROSPERO database (registration number 531468). Results: Seven significant studies were selected and included in our review. Meta-analysis was performed using RevMan. Co-CNN (with incorporated demographic data and clinical variables) demonstrated the highest weighted average sensitivity at 0.84. 2D-CNN models (with demographic features) showed a balanced performance with good sensitivity (0.62) and high specificity (0.82); Co-CNN models excelled in sensitivity (0.84) but had lower specificity (0.71). Traditional ECG criteria (SLV and CV) maintained high specificities but low sensitivities. Scatter plots revealed trends between demographic factors and performance metrics. Conclusions: AI algorithms can rapidly analyze ECG data with high sensitivity. The diagnostic accuracy of AI models is variable but generally comparable to classical criteria. Clinical data and the training population of AI algorithms play a critical role in their efficacy. Future research should focus on collecting diverse ECG data across different populations to improve the generalizability of AI algorithms.

Keywords: artificial intelligence; deep learning; electrocardiogram; left ventricle hypertrophy; machine learning.

Figure 1

The structure of a search strategy (MEDLINE).

Figure 2

Diagnostic accuracy of AI algorithms and classical ECGs criteria. CNN—Convolutional Neural Network, DNN—Deep Neural Network, ENN—Extreme Learning Machine Neural Network; CNN-LSTM—Convolutional Neural Network—Long Short-Term Memory; SL—Sokolow-Lyon; Kwon, 2019 [34]; Kokubo, 2022 [33]; Cai, 2024 [36]; Ryu, 2023 [37]; Zhao, 2022 [12].

Figure 3

Risk of bias. (a) Summary panel; (b) Analysis of the studies: Cai, 2024 [36], Kokubo, 2022 [33], Kwon, 2019 [34], Liu, 2022 [35], Ryu, 2023 [37], Salazar, 2021 [38], Zhao, 2022 [12].

Figure 4

Results of meta-analysis. Cai, 2024 [36]; Kokubo, 2022 [33]; Kwon, 2019 [34]; Liu, 2022 [35]; Ryu, 2023 [37]; Salazar, 2021 [38]; Zhao, 2022 [12].

Figure 5

ROC Curve Overview. CNN — Convolutional Neural Network, DNN—Deep Neural Network, ENN—Extreme Learning Machine Neural Network; CNN-LSTM—Convolutional Neural Net-work—Long Short-Term Memory; SL—Sokolow-Lyon.