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©The Author(s) 2021.
World J Gastroenterol. Jun 14, 2021; 27(22): 2979-2993
Published online Jun 14, 2021. doi: 10.3748/wjg.v27.i22.2979
Published online Jun 14, 2021. doi: 10.3748/wjg.v27.i22.2979
Table 1 Summary of artificial intelligence applications in predicting Helicobacter pylori infection
Ref. | Endoscopic modality | Training dataset | Validation dataset | Accuracy | Sensitivity | Specificity | PPV |
Huang et al[78], 2004 | WLI | 30 patients | 74 patients | 85.1 (avg)1 | 78.8 (avg) | 90.2 (avg) | - |
Shichijo et al[79], 2017 | WLI | 32208 images, 1768 patients | 11481 images, 397 patients | 87.7 | 88.9 | 87.4 | - |
Itoh et al[81], 2018 | WLI | 149 images, 139 patients | 30 images, 30 patients | - | 86.7 | 86.7 | - |
Nakashima et al[84], 2018 | WLI, BLI and LCI | 162 patients | 60 patients | - | 96.7 | - | - |
Shichijo et al[80], 2019 | WLI | 98564 images, 4494 patients | 23699 images, 847 patients | Infected: 66.0; post-eradication: 86.0 | - | - | - |
Zheng et al[82], 2019 | WLI | 11729 images, 1507 patients | 3755 images, 452 patients | 84.5 | 81.4 | 90.1 | - |
Zhu et al[100], 2019 | WLI | 790 images | 203 images | 89.2 | 76.5 | 95.6 | 89.7 |
Nakashima et al[85], 2020 | WLI, BLI and LCI | 12887 images, 395 patients | 120 patients | 80.0 (avg)2 | 61.3 (avg) | 89.4 (avg) | 74.7 (avg) |
Table 2 Summary of artificial intelligence applications in prediction of invasion depth and differentiation of cancerous areas from noncancerous areas
Ref. | Application | Endoscopic modality | Training dataset | Validation dataset | Accuracy | Sensitivity | Specificity | PPV | NPV |
Kubota et al[98], 2012 | Prediction of invasion depth | WLI | 344 patients, 902 images | - | 77.2 (T1) | - | - | 80.1 (T1) | |
Miyaki et al[137], 2013 | Differentiation of cancerous areas from noncancerous areas | WLI and magnified FICE | 493 images | 46 images | 85.9 | 84.8 | 87.0 | 86.7 | 85.1 |
Hirasawa et al[99], 2018 | Differentiation of cancerous areas from noncancerous areas | WLI | 13584 images | 2296 images, 69 patients | 92.2 | 92.2 | - | 30.6 | - |
Kanesaka et al[138], 2018 | Detection of EGC | Magnified NBI | 126 images | 81 images | 96.3 | 96.7 | 95.0 | 98.3 | - |
Horiuchi et al[103], 2020 | Differentiation of EGC from gastritis | Magnified NBI | 2570 images | 258 images | 85.3 | 95.4 | 71.0 | 82.3 | 91.7 |
Yoon et al[101], 2019 | Detection of EGC and prediction of EGC invasion depth | WLI | 11686 images, 800 patients | - | 79.2 | 77.8 | 79.3 | 77.7 | |
Horiuchi et al[105], 2020 | Detection of EGC | Magnified NBI | 2570 images | 174 videos, 82 patients | 85.1 | 87.4 | 82.8 | 83.5 | 86.7 |
Li et al[139], 2020 | Differentiation of EGC from noncancerous lesions | Magnified NBI | 2088 images | 342 images | 90.9 | 91.2 | 90.6 | 90.6 | 91.2 |
Nagao et al[102], 2020 | Prediction of invasion depth | WLI, nonmagnifying NBI and indigo-carmine dye contrast imaging (Indigo) | 16557 images, 1084 patients | - | 94.4 | 89.2 | 98.7 | 98.3 | 91.7 |
Namikawa et al[104], 2020 | Differentiation of cancerous areas from noncancerous areas | WLI, nonmagnifying NBI and indigo-carmine dye contrast imaging (Indigo) | 18410 images | 1459 images | 95.9 | 99.0 | 93.3 | 92.5 | - |
- Citation: Hsiao YJ, Wen YC, Lai WY, Lin YY, Yang YP, Chien Y, Yarmishyn AA, Hwang DK, Lin TC, Chang YC, Lin TY, Chang KJ, Chiou SH, Jheng YC. Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer. World J Gastroenterol 2021; 27(22): 2979-2993
- URL: https://www.wjgnet.com/1007-9327/full/v27/i22/2979.htm
- DOI: https://dx.doi.org/10.3748/wjg.v27.i22.2979