Artificial intelligence for endoscopic correlates of Correa’s cascade in gastric precancerous lesions and early neoplasia

Table 3 Summary of artificial intelligence studies targeting endoscopic assessment of Helicobacter pylori infection

Tier1	Study	Modality	Task and unit	Reference standard2	Dataset and validation3	AI method4	Clinical maturity5	Key results
4A	Itoh et al[23], 2018	WLI	CADx; binary H. pylori infection-status classification; image-level	Serum H. pylori IgG antibody	179 images; retrospective image-based evaluation	CNN (GoogLeNet)	Retrospective image-based study	AUC 0.96; SEN 86.7%; SPE 86.7%
4A	Shen et al[24], 2023	WLI	CADx; binary H. pylori infection-status classification; video-derived case-level	Histopathology and/or UBT	113908 images from 599 videos and 456 patients	CNN (ResNet34 with weakly supervised MIL)	Retrospective image-/video-based study	AUC 0.95; ACC 89.9%; SEN 91.5%; SPE 88.8%
4A	Zou et al[25], 2024	WLI	CADx; binary H. pylori infection-status classification; patient-level	Concordant results from at least two tests (UBT, RUT, culture, or histopathology)	9457 images from 418 patients; randomized controlled trial	CNN (EfficientNet-B0 with SE-Net)	Randomized clinical trial	Test ACC 89.6%; RCT ACC 92.8% with AI vs 75.6% in control group
4A	Yan-Dong et al[26], 2025 (earlier:[82])	WLI	CADx; real-time binary H. pylori infection-status classification; patient-level	UBT	132297 images and videos from 3803 patients plus an additional 701 patients	CNN (site recognition[83] plus EF-CNN/HP-CNN pipeline)	Real-time clinical feasibility	AUC 0.918; ACC 86.3%; SEN 86.9%; SPE 85.9%; procedure duration 283.3 seconds
4A	Hu et al[27], 2025	WLI	CADx; binary H. pylori infection-status classification; case-level	Histopathology from biopsy specimens	308887 images and 197 videos; temporal and geographic external validation	Transformer (PVT-MIL with LSTM aggregation)	External validation; video-level evaluation	AUC 0.903-0.923; ACC 83.3%-85.3%; video ACC 85.3% with AI vs 76.7% for endoscopists
4B	Shichijo et al[28], 2019 (earlier:[84])	WLI	CADx; three-class H. pylori infection-status classification; image-level	UBT, stool antigen testing, blood or urine serology	122263 images; retrospective image-based evaluation	CNN (GoogLeNet)	Retrospective image-based study	Class-wise precision: Negative 80%; post-eradication 84%; positive 48%; runtime 261 s for 23699 images
4B	Nakashima et al[29], 2020 (earlier:[85])	WLI, LCI	CADx; three-class H. pylori infection-status classification; image-/video-level	Serum H. pylori IgG for uninfected and current infection; UBT for post-eradication	12887 images and 240 videos	CNN (separate WLI and LCI DCNNs)	Retrospective image-/video-based study	ACC for uninfected/current/post-eradication: WLI 75.0%/77.5%/74.2%; LCI 84.2%/82.5%/79.2%
4B	Li et al[30], 2025	WLI	CADx; three-class H. pylori infection-status classification with or without eradication history; image-level	UBT, stool antigen testing, serology, PG	44014 images; retrospective image-based evaluation	CNN (multistage DL model with SSL and SKD)	Retrospective image-based study; history-enhanced model	ACC for uninfected/post-eradication/current infection: Image-only model, 87.7%/83.6%/95.9%; history-enhanced model, 92.5%/91.1%/98.6%

¹Tier definitions and clinical decision impact: 4A: Binary Helicobacter pylori infection diagnosis, supporting infection assessment and eradication decision-making; 4B: Multi-class Helicobacter pylori infection diagnosis, supporting differentiation among uninfected, currently infected, and post-eradication phenotypes, which may guide eradication strategies and the interpretation of residual mucosal risk.

²Reference standard indicates the clinical verification method used to assign or confirm the dataset labels.

³Dataset counts refer to non-augmented original images, videos, or patients, when reported. Validation design is summarized as retrospective image-based evaluation, retrospective image-/video-based evaluation, external validation, prospective evaluation, randomized controlled trial, real-time feasibility, or other study designs reported in the original study.

⁴Artificial intelligence method denotes the primary artificial intelligence pipeline used for the main results and is reported at the architecture-family level, with key components noted where relevant. If a study evaluated multiple models, the best-performing or clinically emphasized configuration is reported.

⁵Clinical maturity is categorized according to the highest level of validation or deployment relevance reported, including retrospective image-based study, retrospective image-/video-based study, external validation, video-level evaluation, randomized clinical trial, real-time clinical feasibility, or history-enhanced model.

ACC: Accuracy; AUC: Area under the curve; CADx: Computer-aided diagnosis; CNN: Convolutional neural network; DCNN: Deep convolutional neural network; DL: Deep learning; H. pylori: Helicobacter pylori; IgG: Immunoglobulin G; LCI: Linked-color imaging; LSTM: Long short-term memory; MIL: Multi-instance learning; PG: Pepsinogen; PVT: Pyramid vision transformer; RCT: Randomized controlled trial; RUT: Rapid urease test; SE-Net: Squeeze-and-excitation network; SEN: Sensitivity; SKD: Self-knowledge distillation; SPE: Specificity; SSL: Self-supervised learning; UBT: Urea breath test; WLI: White-light imaging; AI: Artificial intelligence; HP: H. pylori; EF: Endoscopic finding.