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

Table 5 Summary of artificial intelligence studies targeting endoscopic assessment of intestinal metaplasia

Tier1	Study	Modality	Task and unit	Reference standard2	Dataset and validation3	AI method4	Clinical maturity5	Key results
5A	Lin et al[52], 2021	WLI	CADx; three-class gastritis and IM classification; image-level	Biopsy histopathology; updated Sydney system	7037 images; retrospective image-based evaluation	CNN (TResNet)	Retrospective image-based study	CAG ACC 96.24%; IM ACC 97.16%
5A	Wong et al[53], 2022 (earlier:[89,90])	M-NBI	CADx; binary IM classification; image-level	Biopsy histopathology	1516 images; retrospective image-based evaluation	CNN plus BLS (ResNet18 with PAM and stacking ensemble)	Retrospective image-based study	AUC 0.931; ACC 93.2%; TPR 93.6%; TNR 91.2%
5A	Yang et al[54], 2023	WLI, LCI	CADx; binary IM or GA detection; image-level	Biopsy histopathology	21420 images; retrospective multimodal image-based evaluation	CNN (SE-ResNet50 with LAG and DTL)	Retrospective image-based study; multimodal comparison	IM ACC 98.49% with WLI and 99.18% with LCI; GA ACC 95.06% with WLI and 97.12% with LCI
5A	Ligato et al[55], 2024	BLI	CADx; patch-level IM classification with image-level voting	Biopsy histopathology	1384 images; patch-to-image thresholding	CNN (ResNet50 patch classifier with voting)	Retrospective patch-/image-based study	Patch/image ACC 74%/78%; SEN 76%/70%; SPE 72%/100% at a threshold of 0.8
5B	Lai et al[56], 2022	WLI, NBI, M-NBI	CADx; four-class IM severity grading; image-level	Not specified	1175 images; retrospective image-based evaluation	BLS (M3JLBN with multi-scale multi-feature learning and MIL)	Retrospective image-based study; severity grading	ACC 85.0%; SEN 85.1%; PRE 85.2%
5B	Li et al[57], 2023	IEE	CADx; three-class EGGIM grading; site-level	EGGIM-based endoscopic grading without biopsy confirmation	1115 images; retrospective image-based evaluation	CNN (ResNet101 with color-guided deformable convolution)	Offline EGGIM-oriented grading	ACC 84.17%; SEN 83.65%; SPE 92.06%
5B	Niu et al[58], 2024	M-NBI	CADe plus CADx; site-level IM grading and patient-level OLGIM risk stratification	Biopsy histopathology; updated Sydney system; modified OLGIM system	4473 images; internal and external validation	CNN (Faster R-CNN with ResNet50 and SE block)	External validation; patient-level risk stratification	IM grading AUC 0.872 (internal) and 0.803 (external); ACC 80.0% (internal) and 70.4% (external); OLGIM ACC 85.0% (internal) and 76.0% (external)
5B	Almeida et al[59], 2025	NBI	CADx; site-level EGGIM grading and patient-level EGGIM calculation	EGGIM-based endoscopic grading without biopsy confirmation	1280 images cropped into patches; image- and patient-level evaluation	CNN (ResNet50)	Patient-level EGGIM-oriented grading	Per-image ACC 87%; per-patient ACC 88%; SEN 100%; SPE 85%
5C	Wang et al[60], 2019	WLI	Segmentation; IM lesion delineation; pixel-level	Biopsy histopathology	200 images; retrospective image-based evaluation	CNN (DeepLabV3)	Offline segmentation	ACC 89.51%; mIoU 80.67%
5C	Pornvoraphat et al[61], 2024 (earlier:[91,92])	NBI	Segmentation; real-time IM lesion delineation; pixel-level	Biopsy histopathology	4951 images; inference speed reported	CNN (modified BiSeNet with switch adapter)	Real-time segmentation feasibility	ACC 96.31%; SEN 94.09%; SPE 97.07%; IoU 55.55%; 158 FPS
5C	Zhang et al[62], 2026	LCI	Segmentation; IM lesion delineation; pixel-level	Biopsy histopathology	1884 images; inference speed reported	Mamba (VM-UNetV2)	Deployment-oriented segmentation	ACC 86.05%; SEN 78.29%; SPE 88.46%; mIoU 57.02%; 34.61 FPS

¹Tier definitions and clinical decision impact: 5A: Intestinal metaplasia diagnosis, supporting targeted biopsy and identification of precancerous mucosal change; 5B: Intestinal metaplasia severity grading and risk stratification, supporting assessment of disease extent and surveillance planning; 5C: Intestinal metaplasia localization and segmentation, supporting spatial mapping, targeted biopsy, and endoscopic documentation of intestinal metaplasia extent.

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 according to the study design reported in the original article.

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, internal and external validation, image- and patient-level evaluation, real-time segmentation feasibility, deployment-oriented segmentation, and patient-level risk stratification.

ACC: Accuracy; AUC: Area under the curve; BLI: Blue-light imaging; BLS: Broad learning system; CADe: Computer-aided detection; CADx: Computer-aided diagnosis; CAG: Chronic atrophic gastritis; CNN: Convolutional neural network; DTL: Dual transfer learning; EGGIM: Endoscopic Grading of Gastric Intestinal Metaplasia; FPS: Frames per second; GA: Gastric atrophy; IM: Intestinal metaplasia; IoU: Intersection over Union; LAG: Local attention grouping; LCI: Linked-color imaging; MIL: Multi-instance learning; M-NBI: Magnifying narrow-band imaging; mIoU: Mean Intersection over Union; NBI: Narrow-band imaging; OLGIM: Operative Link on Gastric Intestinal Metaplasia Assessment; PAM: Parallel attention module; PRE: Precision; SE: Squeeze-and-excitation; SEN: Sensitivity; SPE: Specificity; TNR: True negative rate; TPR: True positive rate; WLI: White-light imaging.