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

Table 3 Comparison of artificial intelligence/machine learning models and traditional screening methods for diabetic retinopathy

Screening method	Accuracy	Sensitivity	Specificity	Key points
CNNs	High	High	High	Capable of analysing complex retinal images with high accuracy and scalability
Support vector machines	Moderate	Moderate	Moderate	Effective in classifying pre-extracted features but less scalable than CNNs
Random forests	Moderate	Moderate	Moderate	Good for feature extraction-based classification; robust but less flexible
Traditional manual fundus examination	Variable	Low to moderate	Low to moderate	Dependent on the skill of the ophthalmologist; less accessible and scalable

CNN: Convolutional neural network.