BACKGROUND

Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography (FP) and optical coherence tomography (OCT) images allows prompt detection and intervention.

PURPOSE

To evaluate the performance of AI in detecting DME from FP or OCT images and identify potential factors affecting model performances.

DATA SOURCES

We searched seven electronic libraries up to 12 February 2023.

STUDY SELECTION

We included studies using AI to detect DME from FP or OCT images.

DATA EXTRACTION

We extracted study characteristics and performance parameters.

DATA SYNTHESIS

Fifty-three studies were included in the meta-analysis. FP-based algorithms of 25 studies yielded pooled area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of 0.964, 92.6%, and 91.1%, respectively. OCT-based algorithms of 28 studies yielded pooled AUROC, sensitivity, and specificity of 0.985, 95.9%, and 97.9%, respectively. Potential factors improving model performance included deep learning techniques, larger size, and more diversity in training data sets. Models demonstrated better performance when validated internally than externally, and those trained with multiple data sets showed better results upon external validation.

LIMITATIONS

Analyses were limited by unstandardized algorithm outcomes and insufficient data in patient demographics, OCT volumetric scans, and external validation.

CONCLUSIONS

This meta-analysis demonstrates satisfactory performance of AI in detecting DME from FP or OCT images. External validation is warranted for future studies to evaluate model generalizability. Further investigations may estimate optimal sample size, effect of class balance, patient demographics, and additional benefits of OCT volumetric scans.

This article contains supplementary material online at https://doi.org/10.2337/figshare.24518287.

PROSPERO reg. no. CRD4202127609, https://www.crd.york.ac.uk/prospero/

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.
You do not currently have access to this content.