Revolutionizing upper gastrointestinal disease diagnosis: The transformative role of artificial intelligence in endoscopy

Table 1 The summary table of the application progress of artificial intelligence in the diagnosis of upper gastrointestinal diseases

Disease type	Application scenario	Research progress	Key indicators	Notes
BE	Assisted diagnosis, targeted biopsies	Convolutional Neural Network for detecting dysplasia in BE: Sensitivity 91%, specificity 79%, AUC value 93%[6]	Sensitivity: 91%-93.8%; specificity: 79%-90.7%; accuracy: 92.0%	AI significantly improves the detection rate of BE, reducing missed and misdiagnosed cases
		AI model for automatic identification and segmentation of BE extent: IoU values 0.56 and 0.82[7]
		Multicenter real-time endoscopic video detection: Sensitivity 93.8%, specificity 90.7%, accuracy 92.0%[9]
Esophageal cancer	Early lesion detection	CAD system for detecting squamous cell carcinoma: Accuracy 92.9%, comparable to experts[12]	Sensitivity: 93.8%; specificity: 91.73%; accuracy: 92.9%-94%	AI shows excellent performance in early esophageal cancer detection
		DL models: Accuracy 92.9%-94% in NBI mode, 92.9% in white-light mode[13]
		AI-assisted system improves detection rates and reduces missed diagnoses[15]
GERD	Disease classification, lesion segmentation	DL model for LA grading: Accuracy 87.9%-95.7%[19,22]	Accuracy: 87.9%-95.7%; IoU value: 0.9007	AI performs well in GERD grading and lesion segmentation
		XAI model for five-class classification: Accuracy 86.7%, higher than senior endoscopists[23]
		Attention U-Net segmentation model: IoU value 09007[24]
Esophagogastric varices	Real-time diagnosis, bleeding risk prediction	AI system for detecting esophagogastric varices: Accuracy 93.8%, comparable to endoscopists[33]	Accuracy: 93.8%; sensitivity: 80% (bleeding risk prediction); specificity: 74.4%	AI outperforms traditional methods in variceal diagnosis and risk prediction
		DL model based on CT imaging for predicting variceal bleeding risk[34]

BE: Barrett's esophagus; AI: Artificial intelligence; DL: Deep learning; IoU: Intersection over union; AUC: Area under the curve; CAD: Computer-aided diagnosis; CT: Computed tomography; GERD: Gastroesophageal reflux disease.