Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning

Table 2 Summary of artificial intelligence/machine learning techniques in diabetic retinopathy detection

Technique	Description	Advantages	Limitations
CNNs	Deep learning model for image analysis; learns hierarchical features	High accuracy, effective for image data	Requires large datasets, computationally intensive
Support vector machines	Supervised learning model; used for classifying pre-extracted features	Robust with small datasets, interpretable results	Less effective with large-scale image data
Random forests	Ensemble learning method using decision trees; used for feature-based classification	Good performance with noisy data	Requires feature extraction, less flexible than CNNs

CNN: Convolutional neural network.