Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses

Table 1 Comparison of two machine learning models

	Supervised model	Unsupervised model
Date dependency	High-quality annotations	Label-efficient
Clinical applicability	Task-specific optimization	Exploratory applications
Interpretability	Moderate (via feature mapping)	Low (abstract feature hierarchy)
Deployment	Device-specific optimization	Generalizable frameworks

Table 2 Comparison of the effects of machine learning-based endoscopic image recognition in different diseases

	Sensitivity (range)	Specificity (range)	Key influencing factors
Barrett’s esophagus	85%-92%	78%-88%	Complexity mucosal texture, annotation consistency
Early gastric cancer	89%-95%	82%-91%	Lesion size, endoscopic image resolution
Ulcerative colitis	76%-84%	88%-93%	Subjectivity of inflammation activity scoring, data heterogeneity
Colorectal polyps	92%-97%	90%-95%	Polyp morphological diversity, real-time detection algorithm efficiency