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

Table 1 Artificial intelligence application for esophageal squamous cell cancer

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.
Diagnosis	Retrospective	Still image	HRM	2-class LDA	104 sites	167 sites	0.93	84%	95%	NA	NA	NA	NA	Shin et al[18], 2016
Diagnosis	Prospective	Still image	HRM	Fully automated algorithm	104 sites	167 sites	0.937	95%	91%	NA	NA	NA	NA	Quang et al[19], 2016
Detection	Retrospective	Still image	WCE	JDPCA + CCV	400 images	10-fold-CV	0.9471	93.33%	89.20%	90.75%	NA	NA	NA	Liu et al[20], 2016
Diagnosis	Retrospective	Still image	WLI/NBI	CNN-SSD	8428 images	Caffe DL framework/1118 images	NA	81% (WLI)/89% (NBI) (per-patient) 72% (WLI)/86% (NBI) (per-image)	79%	99%	39%	95%	NA	Horie et al[21], 2019
Detection	Retrospective	Still image	WLI	DNN-CAD	2428 images	187 images	0.9637	97.8%	85.4%	91.4%	86.4%	97.6%	Superior	Cai et al[22], 2019
Diagnosis	Retrospective	Still image	WLI/NBI/BLI	CNN-SSD	22562 images	Caffe DL framework/727 images	NA	100% (Non-ME + NBI/BLI) 90% (Non-ME + WLI) 98% (ME)	63% (Non-ME + NBI/BLI) 76% (Non-ME + WLI) 56% (ME)	77% (Non-ME + NBI/BLI) 81% (Non-ME + WLI) 77% (ME)	NA	NA	Equivalent	Ohmori et al[23], 2020
Diagnosis	Retrospective	Still image	ME-NBI	FCN-CAD	1383 lesions	3-fold-CV	NA	87% (lesion level)	84.1 (lesion level)	89.2 (lesion level) 93.0 (pixel level)	NA	NA	Equivalent	Zhao et al[24], 2019
Diagnosis	Retrospective	Still image	ME-NBI	CNN	7046 images	5-fold-CV	NA	89.3%	98%	93.7%	NA	NA	NA	Everson et al[25], 2019
Diagnosis	Retrospective	Still image	ECS	CNN-GoogLeNet	4715 images	Caffe DL framework/1520 images	0.85	92.6%	89.3%	90.9%	NA	NA	NA	Kumagai et al[27], 2019
Invasion depth measurement	Retrospective	Still image	WLI/NBI/BLI	CNN-SSD	14338 images	Caffe DL framework/914 images	NA	90.1%	95.8%	91.0%	99.2%	63.9%	Equivalent	Nakagawa et al[28], 2019
Invasion depth measurement	Retrospective	Still image	WLI/NBI	CNN-SDD-GoogLeNet	1751 images	Caffe DL framework/291 images	NA	84.1%	73.3%	80.9%	NA	NA	Superior	Tokai et al[29], 2020
Diagnosis	Retrospective	Still image/Real-time video	NBI	CAD-SegNet	6473 images	6671 images/80 videos	0.989	98.04% (per-image) 91.5%(per-frame)	95.03% (per-image) 99.9%(per-frame)	NA	NA	NA	NA	Guo et al[30], 2020
Diagnosis	Retrospective/ Prospective	Still image/Real-time image	WLI	GRAIDS/DeepLab V.3+	4091 images	3323 images	NA	NA	NA	NA	NA	NA	Equivalent	Luo et al[31], 2020
Detection/Invasion depth measurement	Retrospective	Still image/Real-time video	NBI/BLI	CNN-SSD	17274 images	5277 images/144 videos	NA	91.1%	51.5%	63.9%	46.1%	92.7%	Superior	Fukuda et al[32], 2020
Invasion depth measurement	Retrospective	Still image/video images	WLI/NBI/BLI	CNN-SSD	23977 images	PyTorch DL framewor/102 video images	NA	50% (non-ME) 70.8%(ME)	98.7% (non-ME) 94.9%(ME)	87.3% (non-ME) 89.2%(ME)	92.3% (non-ME) 81.0%(ME)	86.5% (non-ME) 91.4%(ME)	Superior	Shimamoto et al[33], 2020

Although no detailed information about the classification of esophageal cancer (EC) was found in the research of Liu et al[20] and Luo et al[31], I prefer to summarize the data in this table because most EC patients in China suffer from esophageal squamous cell carcinoma. AI: Artificial intelligence; AUC: Area under the curve; SEN: Sensitivity; SPE: Specificity; ACC: Accuracy; PPV: Positive predictive value; NPV: Negative predictive value; HRM: High-resolution microendoscopy; 2-class LDA: Two-class linear discriminant analysis; NA: Not available; WCE: Wireless capsule endoscopy; JDPCA: Joint diagonalization principal component analysis; CCV: Color coherence vector; n-fold-CV: n-fold cross-validation; WLI: White light imaging; NBI: Narrow-band imaging; CNN: Convolutional neural network; SSD: Single-Shot Multibox Detector; DNN: Deep neural network; DL: Deep Learning; BLI: Blue Laser Imaging; ME: Magnifying endoscopy; FCN-CAD: Full convolutional network computer-aided detection; ECS: Endocytoscopic system; GRAIDS: Gastrointestinal Artificial Intelligence Diagnostic System.

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.