Artificial intelligence-assisted esophageal cancer management: Now and future

Table 2 Computer-aided endoscopic diagnosis for early esophageal squamous cell cancer

Ref.	Year	Study design	Lesions	Diagnostic method	AI technology	Dataset capacity	Validation	Outcomes	Compared to expert	Processing speed
Liu et al[71]	2016	Retrospective	Early ESCC	WLI	JDPCA + CCV	400 images	10-fold cross-validation	Accuracy: 90.75%; AUC: 0.9471; SEN/SPE: 93.33%/89.2%	NA	NA
Horie et al[56]	2019	Retrospective	ESCC	WLI; NBI	CNN-SSD	41 pts (train 8428 images; test 1118 images without histology distinction)	Caffe DL framework	Accuracy: 99%; Per-image SEN: 72%/86% ( WLI/NBI, respectively); Per-case SEN: 79%/89% ( WLI/NBI, respectively)	NA	0.02 s/image
Cai et al[72]	2019	Retrospective	Early ESCC	WLI	DNN	2615 images (train 2428, test 187)	NA	Accuracy: 91.4%; SEN/SPE: 97.8%/85.4%	Superior	NA
Zhao et al[74]	2019	Retrospective	Early ESCC	ME + NBI	Double labeling FNN	1350 images with 1383 lesions	3-fold cross-validation	Accuracy/SEN/SPE at lesion level: 89.2%/87%/84.1%; Accuracy at pixel level: 93%	Comparable	NA
Ohmori et al[73]	2020	Retrospective	Superficial ESCC	ME + NBI/BLI; Non-ME + WLI/NBI/BLI	CNN	23289 images (train 22562, test 727)		Accuracy/SEN/SPE: 77%/100%/63% (Non-ME + NBI/BLI); 81%/90%76% ( Non-ME + WLI); 77%/98%/56% ( ME)	Comparable	0.028 s/image
Nakagawa et al[76]	2019	Retrospective	ESCC (EP-SM1/SM2+SM3)	ME; Non-ME	CNN-SSD	15252 images (train 14338, test 914)	Caffe DL framework	Accuracy/SEN/SPE: 91%/90.1%/95.8%	Comparable	0.033 s/image
Everson et al[77]	2019	Retrospective	ESCC IPCLs (type A/type B)	ME + NBI	CNN	7046 images	5-fold cross-validation+eCAM	Accuracy/SEN/SPE: 93.3%/89.3%/98%	NA	0.026-0.037 s/image
Guo et al[79]	2020	Retrospective	Early ESCC	NBI (ME + non-ME)	CNN-SegNet	13144 images (train 6473, validation 6671), 80 videos (47 lesions, 33 normal esophagus)	NA	Per-image SEN/SPE: 98.04%/95.03%; Per-frame SEN/SPE: 91.5%/99.9%	NA	< 0.04 s/frame; Latency <0.1 s
Shin et al[82]	2015	Retrospective	HGD, ESCC	HRM	Two-class LDA	375 sites of images (train 104, test 104, validation 167)	NA	AUC: 0.95; SEN/SPE: 84%/95%	NA	3.5 s/image
Quang et al[83]	2016	Retrospective	ESCC	HRM	A fully automated algorithm	375 biopsied sites from Shin et al[82] (train 104, test 104, validation 167)	NA	AUC: 0.937; SEN/SPE: 95%/91%	NA	Average 5 s for computing

BLI: Blue laser imaging; CCV: Color coherence vector; DL: Deep learning; CNN: Convolutional neural network; DNN: Deep neural network; eCAM: Explicit class activation map; EP: Epithelium; ESCC: Esophageal squamous cell cancer; FNN: Fuzzy neural network; HGD: High-grade dysplasia; HRM: High-resolution microendoscopy; IPCLs: Intra-papillary capillary loops; JDPCA: Joint diagonalisation principal component analysis; LDA: Linear discriminant analysis; ME: Magnifying endoscopy; NA: Not available; NBI: Narrow band imaging; SEN: Sensitivity; SM: Submucosa; SPE: Specificity; SSD: Single shot multibox detector; WLI: White light imaging.