Early gastrointestinal cancer: The application of artificial intelligence

Table 1 Early gastrointestinal cancer and artificial intelligence

Ref.	Target disease	Prospective/ retrospective	AI	Endoscopy image	Training dataset	Validation dataset	Sensitivity	Specificity	Accuracy1/AUC
[1]	Diagnosing ESCC and EAC	Retrospective	CNNs (SSD)	WLI and NBI	8428 images	1118 images	98%	95%	98%1
[2]	Diagnosing ESCC	Retrospective	CAD (SegNet)	NBI/videos	6473 images	6671 images	98.04%	95.03%	0.989
[3]	Detecting EEC and BE	Retrospective	CAD (ResNet-UNet)	WLI	494364 images	1704 images	90%	88%	89%1
[4]	Detecting E/J cancers	Retrospective	CNNs (SSD)	WLI and NBI	3443 images	232 images	94%	42%	66%1
[5]	Detecting ESCC	Retrospective	DCNNs-CAD	NBI	2428 images	187 images	97.80%	85.40%	91.4%1
[6]	Diagnosing BE and EAC	Retrospective	CAD (ResNet)	WLI and NBI	148/100	Leave-one patient-out cross validation	97%(WLI)/94%(NBI)	88% (WLI)/80%(NBI)
[7]	Diagnosing ESCC	Retrospective	CAD (FCN)	ME-NBI		3-fold cross-validation
[8]	Detecting EAC	Retrospective	CNNs (SSD)	WLI		100 images	96%	92%
[9]	Detecting EGC	Retrospective	CNNs	WLI	348943 images	9650 images	80.00%	94.80%
[10]	Diagnosing EGC	Retrospective	CNNs	WLI	21217 images	1091 images	36.8	91.20%
[11]	Diagnosing EGC	Retrospective	CNNs (Inception-v3)	ME-NBI	1702 images	170 images	91.18%	90.64%	90.91%1
[12]	Diagnosing EGC	Retrospective	CNNs (VGG16)	WLI	896 t1a-EGC and 809 t1b-EGC	5-fold cross-validation			Detection (0.981)
									Depth prediction (0.851)
[13]	Detecting EGC	Retrospective	CNNs (VGG16 and ResNet-50)	WLI/NBI/BLI	3170 images		94.00%	91.00%	92.5%1
[14]	Diagnosing EGC	Retrospective	CNNs (ResNet-50)	WLI	790 images	203 images	76.47%	95.56%	89.16%1
[15]	Detecting EGC	Retrospective	CNNs (SSD)	WLI	13584 images	2940 images	58.40%	87.30%	0.76
[16]	Classifying EGC	Retrospective	CNNs (Inception-ResNet-v2)	WLI	5017 images	5-fold cross-validation			0.85
[17]	Diagnosing EGC	Retrospective	CNNs (ResNet-50)	ME-NBI	4460 images	1114 images	98%	100%	98.7%1
[18]	Detecting and localizing colonic adenoma	Representative	CNNs (VGG16,19, ResNet50)	WLI and NBI	8641 images/9 videos, 11 videos	Cross-validation
[19]	Detecting ECC	Representative	CNNs	WLI	190 images	3-fold cross-validation	67.50%	89.00%	81.2%1/0.871
[20]	Classifying ECC	Representative	CNNs (ResNet-152)	WLI		3-fold cross-validation	95.40%	30.10%
[21]	Detecting colonic adenoma	Prospective	Cade	1058 patients	ADR (29.1% vs 20.3%)
[22]	Detecting colonic adenoma	Prospective	Cade	962 patients	ADR (34% vs 28%)

¹Accuracy is with “¹” and AUC is without “¹”, e.g., 100%¹ means accuracy is 100%.

ADR: Adenoma detection rates; AI: Artificial intelligence; AUC: Area under the curve; BE: Barrett’s esophagus; BLI: Bright light imaging; CAD: Computer-aided diagnosis; CNN: Convolutional neural network; DCNN: Deep convolutional neural network; EAC: Esophageal adenocarcinoma; ECC: Early colonic cancer; EEC: Early esophageal cancer; EGC: Early gastric cancer; E/J: Esophagogastric junctional; ESCC: Esophageal squamous cell carcinoma; ME-NBI: Magnifying narrow band imaging; NBI: Narrow-band imaging; SSD: Single-Shot Multibox Detector; WLI: White-light imaging.