Artificial intelligence-enhanced electrocardiogram for the diagnosis of cardiac amyloidosis: A systemic review and meta-analysis

Abstract

Background

Diagnosis of cardiac amyloidosis (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.

Methods

We searched the Scopus, MEDLINE, and Cochrane CENTRAL databases until April 2024 for studies assessing AI-enhanced ECG diagnosis of CA. Studies reporting findings from derivation and validation cohorts were included. Studies combining other diagnostic modalities, such as echocardiography, were excluded. The outcome of interest was the area under the receiver operating characteristic curve (AUC) for overall CA and subtypes transthyretin amyloidosis (ATTR) and light chain amyloidosis (AL). Analysis was done using RevMan 5.4.1 general inverse variance random effects model, pooling data for AUC and 95 % confidence intervals (CI).

Results

Five studies comprising seven cohorts met the eligibility criteria. The total derivation and validation cohorts were 8,639 and 3,843, respectively, although one study did not describe this data. The AUC was 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.

Conclusion

AI-enhanced ECG models effectively detect CA and may provide a valuable tool for the early detection and intervention of this disease.