Artificial Intelligence-Enhanced Electrocardiogram Is Effective in the Diagnosis of Cardiac Amyloidosis

Artificial intelligence-enhanced electrocardiogram (ECG) models can effectively detect cardiac amyloidosis (CA), according to a study being presented at AHA 2024.

“Diagnosis of (CA) is often delayed due to variability in clinical presentation. The electrocardiogram (ECG) is one of the most common and widely available tools for assessing cardiovascular diseases. Artificial intelligence (AI) models analyzing ECG have recently been developed to detect CA, but their pooled accuracy is yet to be evaluated,” the researchers noted.

In this meta-analysis, the investigators queried Scopus, MEDLINE, and Cochrane CENTRAL databases through April 2024 for studies analyzing AI-enhanced ECG diagnosis of CA. Studies reporting findings from derivation and validation cohorts were included. Overall, this analysis included five studies consisting of seven cohorts. The derivation and validation cohorts comprised 8,639 and 3,843 individuals, respectively.

The findings showed that AUC were 0.89 (95% CI, 0.86-0.91) for cardiac amyloidosis, 0.90 (95% CI, 0.86-0.95) for ATTR amyloidosis and 0.80 (95% CI, 0.80-0.93) for AL amyloidosis. The results prompted the investigators to concluded that “AI-enhanced ECG models effectively detect CA and may provide a useful tool for the early detection and intervention of this disease.”

Reference

Khan L Noor I, Siddique A, et al. Artificial Intelligence-Enhanced Electrocardiogram for the Diagnosis of Cardiac Amyloidosis: A Systemic Review and Meta-analysis. Abstract #Su3112. Presented at the American Heart Association Scientific Sessions 2024; November 16-18, Chicago, Illinois.