Updates on the clinical use of artificial intelligence in upper gastrointestinal diseases: A comprehensive literature review

Table 4 Summary of literature for esophageal cancer

Disease	Ref.	AI Architecture	Training/validation cohort	Targeted endpoint	Key AI performance metrics	Performance compared to humans
Esophageal cancer	[47]	Deep Neural Network-based Computer-Aided Detection (DNN-CADe)	Training: 2428 images (1332 abnormal, 1096 normal) from 746 patients; validation: 187 images from 52 patients	Detection of early esophageal SCC	AUC > 96%; sensitivity 97.8%, specificity 85.4%, accuracy 91.4%	Endoscopists alone: Sensitivity 74.2%, accuracy 81.7%, NPV 79.3%; With DNN-CADe assistance: Sensitivity 89.2%, accuracy 91.1%, NPV 90.4% (all significant)
	[48]	Deep learning (ENDOANGEL-ELD; multiple CNN modules for esophageal lesion detection)	191744 esophagogastroscopic images from multiple institutions (training); 3117 patients enrolled in RCT (1556 experimental, 1561 control)	Detection of high-risk esophageal lesions	System: Sensitivity 89.7%, specificity 98.5%, accuracy 98.2%	HrEL detection rate: AI-assisted 1.8% vs unassisted 0.9% (P = 0.029); early esophageal cancer: 0.7% vs 0.2% (P = 0.031)
	[49]	Deep learning (iCLE system; Inception-ResNet V2 with image recognition, video diagnosis, and quality judgment modules) on pCLE	Training/validation: 25056 images (2803 patients); testing: 2442 images (226 patients); prospective video: 187 videos (67 lesions, 54 patients)	Diagnosis of esophageal squamous neoplasm	Image test: Accuracy 98.3%, sensitivity 95.3%, specificity 98.8%; video: Accuracy 90.9%, sensitivity 92.0%, specificity 90.2%	Expert endoscopists: Sensitivity 80.4%, NPV 87.7% (iCLE superior: P < 0.001, P = 0.003); non-experts with iCLE assistance: Accuracy improved 83.6% to 88.6%, sensitivity 76.0% to 89.8%
	[50]	Deep learning (CNN-based AI system) on EUS images	16110 EUS images from 631 cases (5 SEL categories); 4:1 development-to-test ratio	Classification of upper GI SELs	5-class accuracy 86.1%; GIST vs non-GIST: Sensitivity 98.8%, specificity 67.6%, accuracy 89.3%	AI 5-class accuracy significantly higher than all endoscopists; GIST sensitivity and accuracy significantly higher than all endoscopists (both experts and non-experts)
	[51]	CNN-based real-time detection and segmentation (YOLOv8s-seg + MobileNetv2) on EUS images	1445 esophageal SEL cases	SEL detection and layer identification	Detection: Precision 92.2%, F1 81.9%; layer 2/3 vs 4 differentiation accuracy 76.5%	Senior endoscopists: 74.9%-79.8% (P = 0.585, comparable); Junior endoscopists: 65.6%-66.7% (P = 0.045, AI significantly higher)

AI: Artificial intelligence; DNN: Deep neural network; CADe: Computer-aided detection; SCC: Squamous cell cancer; AUC: Area under the curve; NPV: Negative predictive value; CNN: Convolutional neural network; HrEL: High-risk esophageal lesion; RCT: Randomised controlled trial; iCLE: Intelligent confocal laser endomicroscopy; EUS: Endoscopic ultrasound; SEL: Subepithelial lesion; GI: Gastrointestinal; GIST: Gastrointestinal stromal tumor.