# Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis

## TL;DR

This study reviews and analyzes how well machine learning models detect left ventricular hypertrophy, finding varying accuracy depending on the data and models used.

## Contribution

The study provides a systematic review and meta-analysis of ML-based LVH detection, highlighting performance differences across data types and model types.

## Key findings

- ECG-based models showed a sensitivity of 0.76 and specificity of 0.84 for LVH detection.
- Echocardiography-based models had higher variability in sensitivity and specificity ranges.
- Deep learning models using ECG data had lower sensitivity and specificity compared to other ECG-based models.

## Abstract

In recent years, researchers have investigated machine learning (ML)–based approaches for the detection of left ventricular hypertrophy (LVH). However, the accuracy of ML in detecting LVH varies across different modeling variables and models. Systematic evidence is lacking in understanding how different ML approaches affect LVH detection accuracy.

The aim of this study is to systematically assess the diagnostic accuracy of these ML approaches to inform the development of artificial intelligence tools.

PubMed, Embase, Cochrane Library, and Web of Science were comprehensively searched up to November 12, 2025. The Prediction Model Risk of Bias Assessment Tool was used to evaluate the risk of bias. Subgroup analyses were performed based on ML model types and modeling variables (electrocardiogram [ECG], clinical features, and echocardiography). Only diagnostic 2×2 tables from validation sets were pooled for meta-analysis, with all statistical analyses performed using Stata.

A total of 25 studies were included in the analysis. The performance of ML models varied with input data types and algorithms. A meta-analysis showed that ECG-based models, in comparison, exhibited a sensitivity of 0.76 (95% CI 0.66‐0.84) and a specificity of 0.84 (95% CI 0.78‐0.89). Echocardiography-based models had a sensitivity ranging from 0.71 to 0.94 and a specificity ranging from 0.67 to 0.96. The models based on clinical features had a sensitivity of 0.78 (95% CI 0.69‐0.85) and a specificity of 0.71 (95% CI 0.65‐0.76). A subgroup analysis of the ECG-based models revealed that the deep learning model produced a sensitivity of 0.71 (95% CI 0.60‐0.80) and a specificity of 0.79 (95% CI 0.65‐0.88).

ML demonstrates reasonably high accuracy in detecting LVH. However, these conclusions are derived from limited evidence. Meanwhile, the extreme heterogeneity reported in the meta-analysis requires more critical interpretation. Current conclusions regarding model accuracy should be interpreted with caution. Therefore, future research should focus on constructing high-performance ML models based on imaging data for LVH diagnosis.