Table 5 A literature review for deep learning studies for detecting epiretinal membrane in fundus photography images

Son, 2020 [11]

Local clinic data (ERM, 3639 eyes) + external database

Modified ResNet + classic augmentation

The deep learning model for retinal membrane feature detection showed ROC-AUCs of 0.989 and 0.997 in two validation sets

Casado-García, 2021 [31]

A nationwide database (Spain) + RFMiD

HR-net + CycleGAN

The final model achieved a F1-score of 86.82% to detect ERM

Shao, 2021 [10]

Local clinic data (ERM, 83 eyes / no ERM, 61 eyes)

Inception-Resnet-v2 and Xception + classic augmentation

The AI model achieved an accuracy of 77.1%. It was comparable to manual reading (accuracy, 75.7%)

Kim, 2021 [36]

Local clinic data (ERM, 99 eyes / control, 79 eyes)

ResNet50 + classic augmentation

The deep learning model for ERM detection showed a sensitivity of 92.5% and specificity of 98.3%

Cen, 2021 [12]

JSIEC + LEDRS + EYEPACS

Mask R-CNN + Inception-V3, Xception, InceptionResNet-V2, and modified ResNet and ResNeXt

The final model for ERM detection showed ROC-AUCs of 0.9972 and 0.9976 in two validation sets

Li, 2022 [13]

Local clinic data (ERM, 2947 eyes)

SeResNext50 + classic augmentation

The deep learning model for ERM detection showed ROC-AUCs of 0.968 in the internal validation and 0.938 and 0.934 in the external validation

Son, 2023 [35]

Local clinic data (ERM, 3073 eyes) + MESSIDOR

EfficientNet-B7 + classic augmentation

The deep learning model for membrane feature detection showed ROC-AUCs of 0.997 in the internal validation and 0.954 in the external validation

Ours

Local clinic data (ERM, 302 eyes / control, 1250 eyes) + RFMiD + JSIEC

EfficientNetB0 + StyleGAN2

The proposed model achieved ROC-AUC of 0.926 for internal validation. ROC-AUCs of 0.951 and 0.914 were obtained for the two external validation datasets