Updates on the clinical use of artificial intelligence in upper gastrointestinal diseases: A comprehensive literature review

Table 1 Summary of literature for Barrett’s Esophagus

Disease	Ref.	AI architecture	Training/validation cohort	Targeted Endpoint	Key AI performance metrics	Performance compared to humans
BE	[8]	CNN-based CADe	Benchmarking image test set: 400 images; Benchmarking video test set: 188 videos	Detection of Barrett’s neoplasia	Images: Sensitivity 90%, Specificity 80%; videos: Sensitivity 91%, Specificity 82%	CADe system compared to general endoscopists — Images: Sensitivity 90% vs 74% videos: Sensitivity 91% vs 67%
	[9]	CNN-based CADe	Training: 75198 neoplastic images/videos (96 patients) and 1014973 non-neoplastic images/videos (65 patients); image validation: 471 images (34 patients); video validation: 75 videos (75 patients)	Detection of Barrett’s neoplasia	Sensitivity 93.8%, Specificity 90.7%, accuracy 92.0%	Non-expert endoscopists: Sensitivity 63.5%, specificity 77.9%, Accuracy 71.8% (P < 0.05 for all)
	[10]	CNN-based CADe	Pretraining: 494364 labeled endoscopic images; fine-tuning: 1704 esophageal high-resolution images (669 patients); benchmarking test set: 80 images (80 patients)	Detection of early neoplasia in BE	Sensitivity 93%, specificity 83%, accuracy 88%	53 general endoscopists: Sensitivity 72%, specificity 74%, accuracy 73%, all substantially inferior to the CADe system scores
	[11]	NLP	Development: 600 pathology reports (457 confirmed BE, 60 with dysplasia); validation: 400 reports (230 confirmed BE, 39 with dysplasia)	Identification of dysplasia in BE	Accuracy 98.7%, recall 92.3%, precision 100%	N/A
	[12]	NLP	Development/validation: 426 pathology notes (426 patients); full cohort application: 121808 individuals	Identification of intestinal metaplasia and dysplasia	Overall accuracy 97.6%-100%; BE: Sensitivity 98.9%, specificity 99.6%; gastric IM: Sensitivity 86.8%, specificity 99.0%	N/A
	[13]	CNN ensemble on digitized histology	542 patients (164 NDBE, 226 LGD, 152 HGD); 8596 training bounding boxes, 1946 validation, 840 test	Classification of BE dysplasia grade	Low grade dysplasia: Sensitivity 81.3%, specificity 100%; non-dysplastic BE and high grade dysplasia: Sensitivity and specificity > 90%	N/A
	[14]	Deep learning (CNN-based image and video analysis)	1970 pCLE videos; 897931 biopsy patches; 387 whole-slide images	Classification of BE dysplasia on pCLE	pCLE: Sensitivity for dysplasia 71%, overall accuracy 90%	N/A
	[16]	CNN (pretrained on ImageNet, fine-tuned) with object detection algorithm	916 dysplastic images (65 patients) and 919 non-dysplastic control images; 458 test images (225 dysplasia, 233 non-dysplasia)	Detection of early neoplasia in BE	Sensitivity 96.4%, specificity 94.2%, accuracy 95.4%	N/A
	[17]	CNN (indirectly supervised classifier + directly supervised segmentation/localization model)	Training: 148936 video frames (64 patients); Validation: 25161 images (11 patients); test: 264 i-scan images (44 patients); localization model: Trained on 94 delineated images (30 patients), tested on 86 images (28 patients)	Detection and localization of BE dysplasia	AUC 93%, Sensitivity 91%, specificity 79%; on endoscopist comparison subset: Sensitivity 96%, specificity 88%	6 non-expert endoscopists (same image subset): Sensitivity 79%, specificity 49%
	[18]	CNN (EfficientNetV2B2, pretrained, NBI endoscopic images)	724 cases total; test set: 160 NBI images from 86 patients with histological confirmation	Detection of BE on NBI	Sensitivity 94.3%; specificity 94.4%, accuracy 94.37%	N/A
	[19]	Vision Transformer (ViTBase16, pretrained)	1918 images from 267 patients (classified as dysplastic or non-dysplastic BE)	Classification of BE dysplasia	Sensitivity 97%, specificity 99%, accuracy 99%	N/A

AI: Artificial intelligence; CNN: Convolutional neural network; CADe: Computer-aided detection; BE: Barrett’s esophagus; NLP: Natural language processing; N/A: Not applicable; IM: Intestinal metaplasia; NDBE: Nondysplastic BE; LGD: Low-grade dysplasia; HGD: High-grade dysplasia; pCLE: Probe-based confocal laser endosmicroscopy; AUC: Area under the curve; NBI: Narrow-band imaging.