Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review

Table 1 Summary of studies using deep learning for detection of esophageal precancerous lesions

Ref.	Year	Imaging	Study design	Study aim	DL model	Dataset	Outcomes
Cai et al[30]	2019	WLE	Retrospective	Detection of precancerous lesions and early ESCC	--	2615 images	Sensitivity: 97.8%. Specificity: 85.4%. Accuracy: 91.4%
Guo et al[31]	2020	NBI, M-NBI	Retrospective	Detection of precancerous lesions and early ESCC	SegNet	13144 images and 168865 video frames	Sensitivity: 96.10% for M-NBI videos, 60.80% for non-M-NBI videos, 98.04% for images. Specificity: 99.90% for non-M-NBI/M-NBI videos, 95.30% for images
de Groof et al[32]	2020	WLE	Retrospective	Detection of Barrett’s neoplasia	ResNet/U-Ne	1544 images	Sensitivity: 91%. Specificity: 89%. Accuracy: 90%
de Groof et al[33]	2020	WLE	Retrospective	Detection of Barrett’s neoplasia	ResNet/U-Ne	494364 unlabeled images and 1704 labeled images	Sensitivity: 90%. Specificity: 88%. Accuracy: 89%
Struyvenberg et al[34]	2021	NBI	Retrospective	Detection of Barrett’s neoplasia	ResNet/U-Ne	2677 images	Sensitivity: 88%. Specificity: 78%. Accuracy: 84%
Hashimoto et al[35]	2020	WLE, NBI	Retrospective	Recognition of early neoplasia in BE	Inception-ResNet-v2, YOLO-v2	2290 images	Sensitivity: 96.4%. Specificity: 94.2%. Accuracy: 95.4%
Hussein et al[36]	2020	WLE	Retrospective	Diagnosis of early neoplasia in BE	Resnet101	266930 video frames	Sensitivity: 88.26%. Specificity: 80.13%
Ebigbo et al[37]	2020	WLE	Retrospective	Diagnosis of early EAC in BE	DeepLab V.3+, Resnet101	191 images	Sensitivity: 83.7%. Specificity: 100%. Accuracy: 89.9%
Liu et al[38]	2020	WLE	Retrospective	Detection of esophageal cancer from precancerous lesions	Inception-ResNet	1272 images	Sensitivity: 94.23%. Specificity: 94.67%. Accuracy: 85.83%
Wu et al[39]	2021	WLE	Retrospective	Automatic classification and segmentation for esophageal lesions	ELNet	1051 images	Classification sensitivity: 90.34%. Classification specificity: 97.18%. Classification accuracy: 96.28%. Segmentation sensitivity: 80.18%. Segmentation Specificity: 96.55%, Segmentation accuracy: 94.62%
Ghatwary et al[40]	2021	WLE	Retrospective	Detection of esophageal abnormalities from endoscopic videos	DenseConvLstm, Faster R-CNN	42425 video frames	Sensitivity: 93.7%. F-measure: 93.2%

BE: Barrett’s esophagus; DL: Deep learning; EAC: Esophageal adenocarcinoma; ESCC: Esophageal squamous cell carcinoma; M-NBI: Magnifying narrow band imaging; NBI: Narrow band imaging; WLE: White light endoscopy.