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

Table 2 Summary of artificial intelligence in gastric diseases

Disease	Application	Ref.	Study design	Country/region	Modality	Test set	AI model	Main findings
H. pylori infection	Diagnosis	Martin et al[43]	R	United States	Gastric biopsy	406 patients	CNN	DCNNs accurately recognize gastric pathology damage patterns, especially H. pylori gastritis, and serve as effective screening tools
	Diagnosis	Mohan et al[44]	R	China, Japan	WLI, BLI, LCI	-	CNN	For H. pylori infection diagnosis, CNN achieved 87% accuracy, sensitivity, and specificity, comparable to endoscopists (82.9% accuracy)
	Diagnosis	Nakashima et al[45]	R	Japan	LCI, WLI	515 patients	CNN	Developed LCI/DL-based CAD classifying H. pylori infection into uninfected, active, and post-eradication statuses with 84.2%, 82.5%, 79.2% accuracy. Outperforms WLI and matches expert endoscopists
Gastric polyp	Diagnosis	Yuan et al[46]	R	China	WLI	9443 patients	DCNNs	AI achieved 96.2% accuracy and 88.0% sensitivity for gastric polyps. With AI, junior endoscopists' accuracy significantly improved (96.9%→97.6%), matching seniors
	Diagnosis	Cao et al[47]	R	China	Gastroscopic imaging	2270 images	DL	Improved YOLOv3 with feature fusion boosts small polyp detection in gastroscopic images to 91.6% accuracy, resolving complex background interference
GC	Diagnosis	Horiuchi et al[48]	R	Japan	ME-NBI	2828 images	CNN	CNN system distinguishes EGC from gastritis (sensitivity 95.4%, NPV 91.7%, accuracy 85.3%), aiding clinical diagnosis
	Diagnosis	Li et al[39]	P & R	China	ME-NBI	20341 images	CNN	ME-NBI-based CNN achieves 90.91% accuracy for early GC; 91.18% sensitivity (superior to experts), 90.64% specificity (comparable); overall outperforms non-experts
	Diagnosis	Bu et al[49]	P	China	Liquid biopsy	150 samples	ML	Developing NanoFisher for efficient plasma EV isolation, combining metabolomics and machine learning, achieves 92% accuracy in EGC diagnosis
	Treatment	Wang et al[50]	R	China	CECT	244 patients	ML	CT radiomics distinguishes T2 from T3/T4 GC, guiding neoadjuvant chemotherapy selection
	Treatment	Shang et al[51]	R	China	CECT	311 images	DL	A nomogram built from radiomic and DL features via automated spleen segmentation effectively predicts GC serosal invasion, providing a noninvasive tool for surgical planning
	Treatment	Kang et al[52]	R	South Korea	CT, WLI, biopsy	2927 patients	DL	Developed a Transformer-based multimodal AI system integrating endoscopic images and clinical data. Accurately predicts EGC lymph node metastasis risk (AUC 0.908), guiding treatment decisions
	Treatment	Chen et al[53]	R	China	Laparoscopic surgery video	2460 images	DL	Develop AI models to accurately identify perigastric vessels, enhancing safety and reducing bleeding risks in laparoscopic gastrectomy
	Prognosis	Zhang et al[54]	R	China	CT	669 patients	DCNN	Developed CT-based radiomics nomogram integrating radiomics and clinical factors (e.g., CEA) effectively predicts early recurrence in advanced GC preoperatively (AUC 0.806-0.831)
	Prognosis	Dong et al[55]	R	China, Italy	CECT	730 patients	DL	DLRN accurately predicts GC lymph node metastasis number (C-index 0.797-0.822), outperforming clinical staging and correlating significantly with survival
	Prognosis	Huang et al[56]	R	China	CECT	205 patients	ML	Developed a ML nomogram combining clinical factors (T/N stage) and CT radiomics to predict gastric cancer PNI (validation AUC 0.885), aiding prognosis

R: Retrospective; P: Prospective; ML: Machine learning; LCI: Linked color imaging; CT: Computed tomography; CECT: Contrast-enhanced computed tomography; ME-NBI: Magnifying endoscopy with narrow band imaging; EGC: Early gastric cancer; GC: Gastric cancer; DCNN: Deep convolutional neural networks; H. pylori: Helicobacter pylori; CNN: Convolutional neural network; WLI: White light imaging; BLI: Blue laser imaging; CAD: Computer aided design; AI: Artificial intelligence; EV: Extracellular vesicle; AUC: Area under curve; CEA: Carcinoembryonic antigen; DLRN: Deep learning radiomic nomogram; PNI: Prognostic nutritional index; DL: Deep learning.