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©The Author(s) 2020.
World J Gastroenterol. Sep 28, 2020; 26(36): 5408-5419
Published online Sep 28, 2020. doi: 10.3748/wjg.v26.i36.5408
Published online Sep 28, 2020. doi: 10.3748/wjg.v26.i36.5408
Table 2 Applications of artificial intelligence in pathology and computerized tomography based on different study population
Ref. | Year | Country/region | Number of cases | Study population | Methods | Results |
Li et al[22] | 2018 | China | 700 slices | Publicly gastric slice dataset | GastricNet | Accuracy (100%) |
Sharma et al[23] | 2017 | Germany | 454 cases | Hospital | CNN | Accuracy (0.6990 for cancer classification), accuracy (0.8144 for necrosis detection) |
Leon et al[24] | 2019 | Colombia | 40 images | Department of pathology | Deep CNN | Accuracy (up to 89.72%) |
Iizuka et al[25] | 2020 | Japan | 1746 biopsy histopathology WSIs | Hospital, TCGA | CNN, RNN | AUCs (up to 0.98), accuracy (95.6%) |
Yoshida et al[26] | 2018 | Japan | 3062 gastric biopsy specimens | Cancer center | ML | Overall concordance rate (55.6%) |
Garcia et al[27] | 2017 | Peru | 3257 images | - | Deep CNN | Accuracy (96.88%) |
Liang et al[28] | 2019 | China | 1900 images | - | DL | IoU (0.883), accuracy (91.09%) |
Qu et al[29] | 2018 | Japan | 9720 images/19440 images | Hospital | DL | AUCs (up to 0.965) |
Sun et al[30] | 2019 | China | 500 pathological images | Hospital | DL | IoU (0.8265), accuracy (91.60%) |
Cao et al[31] | 2019 | China | 1399 pathological sections | - | the Mask R-CNN | AP value (61.2) |
- Citation: Niu PH, Zhao LL, Wu HL, Zhao DB, Chen YT. Artificial intelligence in gastric cancer: Application and future perspectives. World J Gastroenterol 2020; 26(36): 5408-5419
- URL: https://www.wjgnet.com/1007-9327/full/v26/i36/5408.htm
- DOI: https://dx.doi.org/10.3748/wjg.v26.i36.5408