Artificial intelligence and early esophageal cancer

Table 1 Application of artificial intelligence in endoscopic detection of Barrett’s esophagus

Ref.	Target disease	Endoscopic modality	AI technology	Database	Outcomes
van der Sommen et al[23], 2016	Early neoplasia in BE	WLI	SVM	100 images	Per-image sensitivity 83%/specificity 83%; Per-patient sensitivity 86%/specificity: 87%
Struyvenberg et al[24], 2021	BE	WLI/NBI	CNN	Train 494364 images/1247 images; test 183 images/157 videos	Images: Accuracy 84%/sensitivity 88%/specificity 78%; Videos: Accuracy 83%/sensitivity 85%/specificity 83%
de Groof et al[25], 2020	Early neoplasia in BE	WLI	ResNet-UNet	Train 1544 images; test 160 images	Dataset 4: Accuracy 89%/sensitivity 90%/specificity 88%; Dataset 5: Accuracy 88%/sensitivity 93%/specificity 83%
de Groof et al[26], 2020	Barrett’s neoplasia	WLI	ResNet-UNet	Train 1544 images; test 20 patients	Accuracy 90%/sensitivity 91%/specificity 89%
Hong et al[27], 2017	BE	Endomicroscopy	CNN	Train 236 images; test 26 images	Accuracy 80.77%
Hashimoto et al[28], 2020	Early neoplasia in BE	WLI/NBI	CNN	Train 1832 images; test 458 images	Accuracy 95.4%/sensitivity 96.4%/ specificity 94.2%
de Groof et al[29], 2019	Barrett’s neoplasia	WLI	SVM	60 images	Accuracy 92%/sensitivity 95%/specificity 85%

BE: Barrett’s esophagus; WLI: White light imaging; SVM: Support vector machine; NBI: Narrow band imaging; CNN: Convolutional neural network.