Artificial intelligence for endoscopic correlates of Correa’s cascade in gastric precancerous lesions and early neoplasia

Table 2 Model families commonly used in endoscopic artificial intelligence

Model family	Core idea	Practical implication
CNN	Learns local image features, including texture, edges, color, and mucosal patterns	Common backbone for classification, detection, and real-time applications
Transformer	Uses attention mechanisms to capture broader spatial or temporal context	Useful for context-aware analysis but more data-intensive and computationally intensive
Mamba	Uses state-space modeling for efficient long-sequence processing	Promising for video analysis and long-context tasks, although clinical validation remains limited
Traditional ML	Uses handcrafted features with classifiers such as support vector machines or random forests	Useful in selected applications but less flexible than end-to-end deep learning
Broad learning system	Uses a wide, expandable network structure	Enables rapid model updating in research settings, although clinical evidence remains limited
Automated deep learning	Automates model selection, hyperparameter tuning, or architecture search	Reduces development burden but may limit model transparency
Hybrid model	Combines multiple model families, such as CNN-transformer or CNN-ML architectures	May improve flexibility but remains dependent on the specific task, training data, labels, and validation strategy

CNN: Convolutional neural network; ML: Machine learning.