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.