TABLE 5.
Research summary of artificial intelligence in age-related macular degeneration.
| Year | Country or region | Authors | Task | Dataset (disease images) | AI algorithm | Output |
|---|---|---|---|---|---|---|
| 2022 | Korea | Han et al. (2022) | Diagnosis | 4,749 images (2,624 images) | VGG-16, VGG-19, ResNet | Accuracy = 0.874 |
| 2021 | America | Tak et al. (2021) | Classification | 420 images (420 images) | Convolutional neural networks | Accuracy = 0.88 |
| 2021 | Taiwan | Chou et al. (2021) | Diagnosis | 699 images (491 images) | EfficientNet-B3 | Accuracy = 0.8367, Sensitivity = 0.8076, Specificity = 0.8472, AUC = 0.8857 |
| 2020 | Korea | Heo et al. (2020) | Diagnosis | 399 images (399 images) | VGG16 | Accuracy = 0.9086 |
| 2022 | America | Ganjdanesh et al. (2022) | Prediction | 30,000 images (30,000 images) | ResNet-18 | Accuracy = 0.905, AUC = 0.762 |
| 2022 | China | Song et al. (2022) | Prediction | 671 images (671 images) | Classified convolution neural network, complete convolution neural network | Accuracy = 0.930, Dice coefficients = 0.873, Sensitivity = 0.873, Specificity = 0.922 |
| 2022 | Taiwan | Yeh et al. (2022) | Prediction | 698 images (698 images) | Deep convolution neural network | AUC = 0.989, Accuracy = 0.936, Sensitivity = 0.933, Specificity = 0.938 |
| 2020 | America | Yan et al. (2020) | Prediction | 31,262 images, 52 related mutated genes (31,262 images) | Convolutional neural networks | AUC = 0.85 |
| 2022 | Austria | Holomcik et al. (2022) | Division | 9,268 images (9,268 images) | U-Net | F1 score = 0.65, Accuracy = 0.75, Recall = 0.72 |
| 2022 | China | He et al. (2022) | Detection | UCSD dataset, Duke dataset (46,421 images) | ResNet-50, Local outlier factor | UCSD: Accuracy = 0.9987 |
| Duke: Accuracy = 0.9756 |