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.