Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step?

Table 2 Artificial intelligence application for esophageal adenocarcinoma

AI Application	Study design	Data category	Type of Images	AI architecture	Training dataset	Validation Method or dataset	AUC	SEN	SPE	ACC	PPV	NPV	Compared with experts	Ref.
Detection	Retrospective	Still image	WLI	CAD-SVM	64 images	LOOCV	NA	95%	NA	75%	NA	NA	NA	van der Sommen et al[36], 2014
Detection	Retrospective	Still image	WLI	CAD-SVM	100 Images	LOOCV	NA	83% (per-image) 86% (per-patient)	83% (per-image) 87% (per-patient)	NA	NA	NA	Inferior	van der Sommen et al[37], 2016
Detection	Retrospective	Still image	VLE	CAD	60 images	LOOCV	0.95	90%	93%	NA	NA	NA	Superior	Swager et al[38], 2017
Detection	Retrospective	Still image	VLE	CAD	60 images	LOOCV	0.90-0.93	NA	NA	NA	NA	NA	Superior	van der Sommen et al[40], 2018
Diagnosis	Retrospective	Still image	WLI/NBI	CNN	8 patients	Caffe DL framework	NA	88% (WLI)/88% (NBI) (per-patient) 69% (WLI)/71% (NBI) (per-image)	NA	90%	NA	NA	NA	Horie et al[21], 2019
Detection	Retrospective	Still image	WLE	CNN-SSD	100 images/39patients	20% patients/5-fold-CV/LOOCV	NA	96%	92%	NA	NA	NA	NA	Ghatwary et al[41], 2019
Detection	Retrospective	Still image	WLI/NBI	CNN- Inception-ResNet-v2	1832 images	458 images	NA	96.4%	94.2%	95.4%	NA	NA	NA	Hashimoto et al[42], 2019
Detection	Retrospective	Still image	WLI	CAD	60 images	LOOCV	0.92	95%	85%	91.7%	NA	NA	NA	de Groof et al[43], 2019
Detection	Retrospective	Still image	WLI	CAD-ResNet-UNet	1544 images	4-fold-CV (internal validation)/160 images (external validation)	NA	87.6% (internal validation) 92.5% (external validation)	88.6% (internal validation) 82.5% (external validation)	88.2% (internal validation) 87.5% (external validation)	NA	NA	NA	de Groof et al[44], 2019
Diagnosis	Retrospective	Still image	WLI/NBI	CAD-ResNet	248 images	LOOCV	NA	97% (WLI)/94% (NBI) (Augsburg data)92% (MICCAI)	88% (WLI)/80% (NBI) (Augsburg data)100% (MICCAI)	NA	NA	NA	NA	Ebigbo et al[45], 2019
Diagnosis	Retrospective	Random images from real-time video	WLI	CAD-ResNet-/DeepLab V.3+	129 images	36 images (real time)	NA	83.7%	100%	89.9%	NA	NA	NA	Ebigbo et al[49], 2020
Surveillance	Prospective	Real-time image	WLI/NBI/VLE	IRIS	NA	Real-time image	NA	NA	NA	NA	NA	NA	NA	Trindade et al[50], 2019
Detection	Prospective	Live endoscopic procedure	Live endoscopic procedure	CAD-ResNet/U-Net	1544 images	48 levels/144 images/20 live endoscopic procedure	NA	90.9% (per level) 75.8% (per image) 90% (per patient)	89.2% (per level) 86.5% (per image) 90% (per patient)	89.6% (per level) 84.0% (per image) 90% (per patient)	NA	NA	NA	de Groof et al[51], 2020

AI: Artificial intelligence; AUC: Area under the curve; SEN: Sensitivity; SPE: Specificity; ACC: Accuracy; PPV: Positive predictive value; NPV: Negative predictive value; WLI: White light imaging; CAD: Computer-aided diagnosis; SVM: Support vector machines; LOOCV: Leave-one-out cross-validation; NA: Not available; VLE: Volume laser endoscopy; NBI: Narrow-band imaging; CNN: Convolutional neural network; DL: Deep learning; SSD: Single-Shot Multibox Detector; n-fold-CV: n-fold cross-validation; MICCAI: Medical Image Computing and Computer Assisted-Intervention; IRIS: Intelligent real-time image segmentation.