Copyright ©The Author(s) 2020.
World J Gastroenterol. Oct 21, 2020; 26(39): 5959-5969
Published online Oct 21, 2020. doi: 10.3748/wjg.v26.i39.5959
Table 1 Application of artificial intelligence in endoscopic detection of early esophageal cancer
Ref.ModalityAI techniqueNo. of images/cases in training dataset (positive/negative)No. of images/cases in test dataset (positive/negative)Results
van der Sommen et al[14], 2016HD-WLESVM100 (60 early BE neoplasia/40 BE)100 (60 early BE neoplasia/40 BE)Sensitivity 83%/specificity 83%
de Groof et al[15], 2019HD-WLESVM60 (40 early BE neoplasia/20 BE)60 (40 early BE neoplasia/20 BE)Sensitivity 95%/specificity 85%
Ebigbo et al[16], 2019HD-WLECNN100 (50 early BE neoplasia/50 BE)100 (50 early BE neoplasia/50 BE)Sensitivity 92%/specificity 100%
Ebigbo et al[16], 2019HD-WLE/NBICNN148 (early BE neoplasia/BE)148 (early BE neoplasia/BE)HD-WLE sensitivity 97%/specificity 88%; NBI sensitivity 94%/specificity 80%
Hashimoto et al[17], 2020WLE/NBICNN1374 (early BE neoplasia/BE)253 WLE (146 early BE neoplasia/107 BE) 205 NBI (79 early BE neoplasia/126 BE)WLE sensitivity 98.6%/specificity 88.8%; NBI sensitivity 92.4%/specificity 99.2%
de Groof et al[18], 2020HD-WLEResNet-UNet1247 WLE + 297 HD-WLE (early BE neoplasia/BE)80 (40 early BE neoplasia/40 BE)Sensitivity 90%/specificity 88%
Swager et al[22], 2017VLESVM60 (30 early BE neoplasia/30 BE)60 (30 early BE neoplasia/30 BE)Sensitivity 90%/specificity 93%
Veronese et al[20], 2013CLESVM337 (23 GM/263 IM/51 neoplasia)337 (23 GM/263 IM/51 neoplasia)Sensitivity 96%/95%/100%
Ghatwary et al[23], 2019CLESVM262 (GM/IM/neoplasia)262 (GM/IM/neoplasia)Sensitivity 70%/93%/93%
Hong et al[24], 2017CLECNN236 (26 GM/155 IM/55 neoplasia)26 (4 GM/17 IM/5 neoplasia)Sensitivity 0%/100%/80%
Ebigbo et al[25], 2020HD-WLECNN129 (early BE neoplasia/BE)62 (36 early BE neoplasia/26 BE)Sensitivity 83.7%/specificity 100%
Cai et al[26], 2019WLECNN2428 (1332 early ESCC/1096 healthy control)187 (91 early ESCC/96 healthy control)Sensitivity 97.8%/specificity 85.4%
Ohmori et al[27], 2020WLE/NBISingle Shot MultiBox Detector22562 (17435 superficial ESCC/5127 control)727 (255 WLE/268 non-magnifying NBI/204 magnifying NBI)WLE sensitivity 90%/specificity 76%; non-magnifying NBI sensitivity 100%/specificity 63%; magnifying NBI sensitivity 98%/specificity 56%
Zhao et al[31], 2019Magnifying NBIDouble-labeling fully convolutional network1383 (207 type A IPCL/970 type B1 IPCL/206 type B2 IPCL)1383 (207 type A IPCL/970 type B1 IPCL/206 type B2 IPCL)Sensitivity 71.5%/91.1%/83.0%
Nakagawa et al[32], 2019WLE/NBICNN8660 non-magnifying (7230 EP-SM1/1430 SM2/3); 5678 magnifying (4916 EP-SM1/762 SM2/3)914 (405 non-magnifying/509 magnifying)Non-magnifying sensitivity 95.4%/specificity 79.2%; magnifying sensitivity 91.6%/specificity 79.2%
Tokai et al[33], 2020WLE/NBICNN1751 superficial ESCC291 (201 EP-SM1/90 SM2)Sensitivity 84.1%/specificity 73.3%
Shin et al[36], 2015HRMETwo-class linear discriminant analysis104 (15 early ESCC/89 control)167 (19 early ESCC/148 control)Sensitivity 84%/specificity 95%
Quang et al[37], 2016HRMETwo-class linear discriminant analysis104 (15 early ESCC/89 control)3 (1 early ESCC/2 control)Sensitivity 100%/specificity 100%
Everson et al[38], 2019magnifying NBICNN7046 sequential images (squamous cell neoplasia/healthy control)7046 sequential images (squamous cell neoplasia/healthy control)Sensitivity 89.7%/specificity 96.9%
Guo et al[39], 2020NBISegNet6473 (early ESCC/control)47 (27 non-magnifying videos/20 magnifying videos)Non-magnifying NBI sensitivity 60.8%; magnifying NBI sensitivity 96.1%