Estimating LVEF from ECG with GPT-4o Fine-Tuned Vision: A Novel Approach in AI-Driven Cardiac Diagnostics

Assessing Left Ventricular Ejection Fraction (LVEF) is crucial for diagnosing reduced systolic function, yet echocardiography (ECHO) may not always be readily available, potentially delaying treatment. Electrocardiography (ECG) offers a cost-effective and accessible alternative for estimating LVEF. However, specialized AI models for this purpose are often complex and costly to develop.

This study uniquely evaluates GPT-4o Fine-Tuned Vision (GPT-4o-FTV), a general-purpose AI model, for detecting LVEF ≤ 35% from ECG images, comparing its performance with a Convolutional Neural Network (CNN) model and clinician assessments.

We analyzed ECGs from 202 patients (42.6% women, mean age 64.5 ± 16.3 years) at a tertiary center, excluding those with pacemakers and including only high-quality ECGs. LVEF ≤ 35% was present in 11.9% (n = 24). GPT-4o-FTV, trained on 20 labeled ECGs, was tested using a structured prompt across four runs. Accuracy, sensitivity, specificity, and positive predictive value (PPV) were compared to a CNN model and four clinicians.

GPT-4o-FTV achieved 79.9% accuracy, 72.9% sensitivity, 80.8% specificity, an F1-score of 46.4%, and a PPV of 34%, outperforming clinicians (74.9% accuracy, 65.6% sensitivity, 76.1% specificity, 39% F1-score, PPV 27.9%). The CNN model had the highest performance (89.1% accuracy, 79.2% sensitivity, 90.4% specificity, 63.3% F1-score, PPV 52.8%).

GPT-4o-FTV demonstrates strong potential as an accessible tool for cardiac diagnostics, particularly in resource-limited settings. While CNN models remain superior in accuracy, the ease of fine-tuning GPT-4o-FTV highlights its practical utility. Future research should focus on larger datasets, additional optimization, and exploring its ability to detect early predictors of LVEF decline.