Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis

Table 1 Summary of artificial intelligence in esophageal diseases

Disease	Application	Ref.	Study design	Region/country	Modality	Test set	AI model	Main findings
BE	Diagnosis	Rosenfeld et al[19]	R	United Kingdom	Questionnaires	1299 patients	ML	ML model with 8 factors (e.g., age) predicts BE (AUC 0.86/0.81), facilitating high-risk screening
	Diagnosis	Abdelrahim et al[20]	P	Europe	WLI	75 patients	CNN	An AI system detected Barrett's neoplasia in real-time endoscopy with 93.8% sensitivity, significantly higher than endoscopists (63.5%)
	Diagnosis	Struyvenberg et al[21]	R	Europe	NBI	157 videos	CNN	Developed a DL-based CAD system for Barrett's neoplasia in NBI videos: 83% accuracy, 85% sensitivity, 83% specificity, processing at 38 fps
	Diagnosis	Hashimoto et al[22]	R	United States	WLI, NBI	1832 images	CNN	AI detects Barrett's early neoplasia at 95.4% accuracy, 96.4% sensitivity via CNN, with real-time lesion localization
Esophageal carcinoma, ESCC	Diagnosis	Tokai et al[23]	R	Japan	WLI, NBI	2042 images	CNN	AI outperformed 13 endoscopists in assessing ESCC invasion depth (accuracy: 80.9%; AUC 0.7873), demonstrating superior diagnostic capability
	Diagnosis	Fukuda et al[24]	R	Japan	NBI, BLI	28333 images	CNN	AI outperformed endoscopists in ESCC detection sensitivity (91% vs 79%) and characterization accuracy (88% vs 75%)
	Diagnosis	Li et al[25]	R	China	WLI, NBI	759 patients	DL	CAD-NBI surpasses CAD-WLI in accuracy/specificity for early ESCC (P < 0.05). Endoscopist combination yields optimal diagnosis (94.9% accuracy, 92.4% sensitivity, 96.7% specificity)
	Diagnosis	Guo et al[26]	R	China	NBI	13144 images	DL	This DL model demonstrates high sensitivity (image 98.04%, video 96.1%) and specificity (image 95.03%, video 99.9%) in real-time diagnosis of esophageal precancerous and early SCC
	Diagnosis	Ohmori et al[27]	R	Japan	WLI, NBI/BLI, ME	21597 images	CNN	AI detected ESCC via non-magnifying endoscopy (NBI/BLI) with 100% sensitivity. With magnification, accuracy reached 83%, comparable to expert endoscopists

R: Retrospective; P: Prospective; ML: Machine learning; CNN: Convolutional neural network; DL: Deep learning; WLI: White light imaging; NBI: Narrow band imaging; BLI: Blue laser imaging; ME: Magnifying endoscopy; AUC: Area under curve; CAD: Computer aided design; ESCC: Esophageal squamous cell carcinoma; SCC: Squamous cell carcinoma; AI: Artificial intelligence; BE: Barrett's esophagus.