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

Table 6 Summary of artificial intelligence studies targeting endoscopic diagnosis of early gastric neoplasia

Tier1	Study	Modality	Task and unit	Reference standard2	Dataset and validation3	AI method4	Clinical maturity5	Key results
6A	Cho et al[69], 2019	WLI	CADx; five-class neoplasia classification; image-level	Biopsy histopathology	5217 images; retrospective image-based evaluation	CNN (inception-ResNet-v2)	Retrospective image-based study	ACC 84.6%; elapsed time 00264 second/image
6A	Gong et al[70], 2023 (earlier:[93])	WLI	CADx and segmentation; six-class disease classification and IM/atrophy delineation	Biopsy histopathology	13672 images; external test set and prospective real-world clinical evaluation	CNN (YOLOv3, EfficientNet, and UNet++ multitask pipeline)	External validation; prospective evaluation	Classification ACC 85.3% (external) and 89.3% (prospective); segmentation dice coefficient 84.7%-84.9%
6A	Kim et al[71], 2024	WLI	CADe and CADx; six-class lesion detection and tumor-depth stage estimation	Biopsy histopathology; resection specimens for cancers, when available	10181 images; retrospective image-based evaluation	CNN (YOLOv7 detector plus T-stage model)	Retrospective image-based study; depth estimation	Detection ACC 73.43%; SEN 80.90%; SPE 83.32%; T-stage ACC 85.17%
6B	Wu et al[72], 2021 (earlier:[94])	WLI	CADe and CADx; focal lesion detection and binary neoplasia diagnosis	Biopsy histopathology; revised Vienna classification	1812 patients; randomized AI-first vs routine-first design	CNN (ENDOANGEL-LD)	Randomized clinical trial	Lesion miss rate 61% with AI-first vs 27.3% with routine-first; first-examination detection rate 49% vs 3.5%
6B	Du et al[73], 2023	WLI, WMI	CADx; binary neoplasia diagnosis; image-/video-level	Biopsy histopathology	4201 images, 7436 paired images, and 162 videos; image, video, and prospective evaluation	CNN (ResNet50)	Prospective evaluation; video-level evaluation; assistance study	Image/video/prospective ACC 86.54%/90.00%/93.55%; non-expert ACC improved from 70.75% to 85.75%
6B	Dong et al[74], 2023	WLI	CADx; binary neoplasia diagnosis; image-/video-level	Biopsy histopathology	4482 images and 296 videos; external image, external video, and consecutive video evaluation	CNN plus ML (DCNN and YOLOv3 features with random forest)	External validation; consecutive video evaluation	External image/video/consecutive video ACC 77.17%/88.24%/79.76%; SEN 87.30%/97.06%/88.24%; SPE 73.08%/82.35%/77.61%
6B	Feng et al[75], 2025	WLI	CADe and CADx; real-time EGC detection and binary EGC diagnosis	Post-ESD histopathology	13289 images and 130 videos; image- and video-based evaluation	CNN (CenterNet with ResNet18)	Real-time video feasibility	Image/video AUC 0.917/0.930; SEN 93.38%/96.92%; SPE 90.07%/89.23%; inference time 0028 second/image

¹Tier definitions and clinical decision impact: 6A: Dysplasia-explicit neoplasia diagnosis, supporting differentiation of clinically adjacent stages in the gastric carcinogenesis cascade and informing biopsy, referral, and therapeutic evaluation; 6B: Composite neoplasia diagnosis, supporting safety-oriented detection of clinically significant lesions and reducing missed neoplasia, but with limited granularity for treatment selection.

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, external validation, prospective evaluation, randomized clinical trial, real-time video feasibility, video-level evaluation, assistance study, or depth estimation.

ACC: Accuracy; AI: Artificial intelligence; AUC: Area under the curve; CADe: Computer-aided detection; CADx: Computer-aided diagnosis; CNN: Convolutional neural network; DCNN: Deep convolutional neural network; EGC: Early gastric cancer; ESD: Endoscopic submucosal dissection; IM: Intestinal metaplasia; ML: Machine learning; SEN: Sensitivity; SPE: Specificity; WLI: White-light imaging; WMI: Weak magnification imaging.