Artificial intelligence in gastroenterology practice: Current perspectives and emerging prospects

Table 2 Studies evaluating applications of artificial intelligence in gastric diseases

Ref.	Clinical application	Data type	AI method	Sample size	Study design, center design, and validation design	Major performance metric	Principal limitation
Shichijo et al[32]	Detection of H. pylori infection	Endoscopic images (WLI)	22-layer deep CNN with GoogLeNet	32208 images (development), 11481 images from 397 patients (test)	Retrospective, single-center, independent test set comparison	Accuracy: 87.7%, 11481 images analyzed in approximately 3.2 minutes vs approximately 230 minutes by endoscopists	Single-center study
Liu et al[35]	Kyoto gastritis classification	Gastric endoscopy images (WLI and NBI)	Five GAM-EfficientNet models	29013 images from 2087 patients, test set of 528 images, compared with 10 endoscopists	Retrospective, single-center, internal training, validation, and testing sets	Average accuracy: 78.70%	Single-center study, imbalanced sample distribution, lack of paired histological validation
Hirasawa et al[36]	Gastric cancer detection	Endoscopic images (WLI, chromoendoscopy and NBI)	CNN with Single Shot MultiBox Detector	13584 training images, 2296 test images	Retrospective, multicenter training, independent test set of consecutive patients	Sensitivity: 92.2%, detected 100% of invasive cancers and 98.6% of lesions ≥ 6 mm, processed 2296 images in 47 seconds	Test set included only cancer cases with high disease prevalence, single endoscope type used
Ikenoyama et al[37]	Early gastric cancer detection	Endoscopic images (WLI)	CNN with Single Shot MultiBox Detector	13584 training images, 2940 test images	Retrospective multicenter training, single-center test set, independent comparison with 67 endoscopists	Sensitivity: 58.4% (95%CI: 51.4%-65.1%) in independent external test set, reading time 45.5 seconds vs 173 minutes for endoscopists	Test set limited to a single center and still images, low PPV of 26.0%
Luo et al[38]	Esophageal and gastric cancer detection	Endoscopy images (WLI)	Deep learning with GRAIDS	1036496 images from 84424 individuals	Multicenter case-control diagnostic study, internal, prospective, and external validation	Accuracy: 0.915-0.977 (external validation sets)	Only standard WLIs were used
Zhang et al[40]	Early gastric cancer detection	Gastroscopic images	Improved Mask R-CNN	1120 images of EGC (140 healthy, 980 EGC cases)	Retrospective, single-center, fivefold cross-validation and testing dataset	Accuracy: 93.9% (internal validation and testing dataset)	Small primary dataset from a single center, generalizability requires further verification
Bang et al[39]	Prediction of submucosal invasion in gastric neoplasms	WLI endoscopic images	Automated DL	2899 training images, 1597 prospective test images	Multicenter validation, training from one hospital and testing at another, prospective multicenter external test	Accuracy: 89.3% (95%CI: 85.1%-93.5%) (external validation), AI-assisted trainee accuracy 81.6% vs 68.4% without AI	Training images from a single institution
Gong et al[42]	Gastric neoplasm detection and classification	Real-time endoscopy	DL clinical decision support system	5017 training images, 2524 pilot procedures	Randomized pilot study and prospective multicenter external test	Classification accuracy: 81.5% (prospective multicenter external test)	Improvement in real-time lesion detection rate was not statistically significant
Wu et al[43]	Gastric neoplasm detection	Real-time white-light endoscopy	DL system	1812 patients	Single-center tandem RCT	Miss rate: 6.1% with AI vs 27.3% with routine examination	Single-center design, endoscopists were not blinded to randomization
Xu et al[44]	Detection of precancerous lesions and adenomas	Digestive endoscopy	ENDOANGEL	Prospective intervention phases	Prospective, randomized, single-center clinical trial	IM detection rate: 14.23% vs 9.15%	Single-center trial design
Calder et al[45]	Automated artifact detection and signal recovery in gastric body-surface mapping	High-resolution gastric myoelectrical activity data from BSGM	Automated artifact detection and rejection algorithm with signal recovery	10 patients, 176 data samples	Prospective validation study, single-center	Accuracy: 95%, sensitivity: 96%, specificity: 95% against expert markers	Small sample size and demographic bias, with all participants being female

H. pylori: Helicobacter pylori; WLI: White-light imaging; CNN: Convolutional neural network; NBI: Narrow-band imaging; GAM: Global attention mechanism; CI: Confidence interval; PPV: Positive predictive value; GRAIDS: Gastrointestinal artificial intelligence diagnostic system; EGC: Early gastric cancer; DL: Deep learning; AI: Artificial intelligence; RCT: Randomized controlled trial; IM: Intestinal metaplasia; BSGM: Body surface gastric mapping.