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

Table 7 Summary of artificial intelligence studies targeting endoscopic delineation and depth staging of early gastric neoplasia

Tier1	Study	Modality	Task and unit	Reference standard2	Dataset and validation3	AI method4	Clinical maturity5	Key results
6C	Ling et al[76], 2021	M-NBI	CADx and segmentation; differentiation status and horizontal margin delineation	Histopathology for EGC differentiation; margin reference from EGC cases	5757 images and 2 videos; test set and man-machine comparison	CNN (VGG16 plus UNet++)	Retrospective image-based study; man-machine comparison	Differentiation ACC 83.3% in test set and 86.2% in man-machine comparison; margin ACC 82.7% for differentiated lesions and 88.1% for undifferentiated lesions at overlap threshold of 0.80
6C	Takemoto et al[77], 2023 (earlier:[95])	WLI	CADe and segmentation; EGC detection and heatmap-based extent demarcation	Histopathology from ESD specimen	1158 images; retrospective image-based evaluation	CNN (GoogLeNet patch classifier with sliding-window heatmap)	Retrospective patch/image segmentation	Image-level SEN 83.8%; SPE 77.5%; case-level successful detection 94.9%; mIoU 66.5%
6C	Ma et al[78], 2023	WLI, M-NBI, CE, NBI	CADx and segmentation; EGC diagnosis and lesion-region delineation	Biopsy histopathology	4697 images; retrospective multimodal image-based evaluation	CNN (GAIN-ResNet50 plus attention U-Net segmentation)	Retrospective image-based study; multimodal segmentation	Classification ACC 98.84%; SEN 97.38%; SPE 99.53%; segmentation PA 83.51%; IoU 0.64
6D	Bang et al[79], 2021 (earlier:[96])	WLI	CADx; mucosal vs submucosal invasion; image-level	Biopsy or resection histopathology	4702 images; two external test sets	CNN (Neuro-T AutoDL)	External validation; depth estimation	External test ACC 89.3% and 88.6%
6D	Wu et al[80], 2022	WLI, M-NBI	CADe and CADx; neoplasm detection, EGC diagnosis, invasion-depth estimation, and differentiation prediction	Biopsy histopathology; resection specimens for cancers, when available	68177 images and 100 videos; AI vs expert comparison	CNN (YOLOv3 plus ResNet50 models)	Video evaluation; expert comparison; multi-task treatment planning	AI vs experts ACC: WLI neoplasm 91.00% vs 76.91%; M-NBI EGC 89.00% vs 85.67%; invasion depth 78.57% vs 63.75%; differentiation 71.43% vs 64.41%
6D	Goto et al[81], 2023	WLI	CADx; mucosal vs submucosal invasion; image-level	Resection or surgical histopathology	700 images; AI, endoscopist, and human-AI cooperation comparison	CNN (EfficientNetB1)	Human-AI cooperation study; depth estimation	ACC 72.5% for AI, 70.0% for endoscopists, and 78.0% for human-AI cooperation; SEN 74.0%, 52.0%, and 76.0%; SPE 71.0%, 88.0%, and 80.0%

¹Tier definitions and clinical decision impact: 6C: Early gastric cancer delineation and extent mapping, supporting lesion-margin definition, biopsy or marking guidance, and endoscopic resection planning; 6D: Invasion-depth estimation and curability-related characterization, supporting selection between endoscopic resection and surgical management.

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; AutoDL: Automated deep learning; CADe: Computer-aided detection; CADx: Computer-aided diagnosis; CE: Chromoendoscopy; CNN: Convolutional neural network; EGC: Early gastric cancer; ESD: Endoscopic submucosal dissection; GAIN: Guided Attention Inference Network; IoU: Intersection over Union; M-NBI: Magnifying narrow-band imaging; mIoU: Mean Intersection over Union; NBI: Narrow-band imaging; PA: Per-pixel accuracy; SEN: Sensitivity; SPE: Specificity; WLI: White-light imaging.