	Shahriar et al., 2022 [22]	USA	542 cases	Whole slide imaging	DL	ResNet101 model: Sensitivity and specificity for LGD are 81.3% and 100% respectively, while for NDBE and HGD they are both > 90%.	ResNet101 model can predict dysplasia grade on whole slide imaging.
	Wen et al., 2022 [23]	China	187 images	Endoscopic images	Fully Convolutional Networks (FCN)	Intersection over Union (IOU) values of 0.56 (GEJ) and 0.82 (SCJ).	The segmentation results of fully automatic DL method are consistent with manual evaluation.
	Alanna et al., 2020 [24]	Germany	129 images	Endoscopic images	CNN and DeepLab V.3+ residual network (ResNet) architecture	AI system has sensitivity and specificity of 83.7% and 100.0% respectively, with an overall accuracy of 89.9%.	This is the first real-time application of deep learning AI system for evaluating and diagnosing early EAC in real-life scenarios.
	Manon et al., 2021 [25]	Netherlands	57 cases	Mass spectrometry imaging (MSI) and hematoxylin and eosin (H & E) staining imaging	ML classifier	Differentiating epithelial tissue from stroma: AUC of 0.89 (MSI) and 0.95 (H & E); distinguishing dysplasia grade: AUC of 0.97 (MSI) and 0.85 (H & E); low-grade progressors and non-progressors: accuracy of 0.72 (MSI) and 0.48 (H & E).	H & E-based classifier excels in differentiating tissue types, while MSI-based model is more accurate in distinguishing dysplasia grade and risk of progression.
	Sharib et al., 2021 [26]	UK	131 videos	Endoscopic high-definition videos	Depth Estimation Networks	Accuracy of phantom endoscopic videos for C & M and island measurements is 97.2%, with an average deviation of ±0.9 mm, while for BEA it is 98.4% with an average deviation of ±0.4 cm.	The quantification system can automatically measure C & M score, quantify Barrett’s epithelium area (BEA), and measure island area, enabling esophageal 3D reconstruction.