Copyright
©The Author(s) 2022.
Artif Intell Cancer. Apr 28, 2022; 3(2): 17-26
Published online Apr 28, 2022. doi: 10.35713/aic.v3.i2.17
Published online Apr 28, 2022. doi: 10.35713/aic.v3.i2.17
Ref. | Study design | Endoscopic modality | Main study aim | Subjects for validation |
Kubota et al[53], 2012 | Retrospective | WLI | Prediction of invasion depth | 344 patients |
Miyaki et al[63], 2013 | Retrospective | ME-FICE | Differentiation of cancerous areas from non-cancerous areas | 46 patients |
Miyaki et al[64], 2015 | Retrospective | ME-BLI | Differentiation of cancerous areas from non-cancerous areas | 95 patients |
Kanesaka et al[65], 2018 | Retrospective | ME-NBI | Delineation of cancerous areas | 81 images |
Hirasawa et al[14], 2018 | Retrospective | WLI, CE, NBI | Delineation of cancer | 69 patients |
Zhu et al[54], 2019 | Retrospective | WLI, NBI | Prediction of invasion depth | 203 lesions |
Cho et al[50], 2019 | Prospective validation dataset | WLI | Differentiation of cancerous areas from non-cancerous areas | 200 patients |
Ishioka et al[55], 2019 | Retrospective | WLI | Detection of GC | 62 patients |
Yoon et al[56], 2019 | Retrospective | WLI | Detection of GC | 800 patients |
Tang et al[57], 2020 | Retrospective | WLI | Differentiation of cancerous areas from non-cancerous areas | 279 patients |
Namikawa et al[58], 2020 | Retrospective | WLI | Differentiation of cancerous areas from non-cancerous areas | 220 lesions |
Li et al[66], 2020 | Retrospective | ME-NBI | Detection of cancer | 341 images |
An et al[62], 2020 | Retrospective | WLI, CE, ME-NBI | Delineation of EGC margins | 355 images |
Horiuki et al[67], 2020 | Retrospective | ME-NBI | Differentiation of cancerous areas from non-cancerous areas | 258 images |
Nagao et al[45], 2020 | Retrospective | WLI, CE, NBI | Prediction of invasion depth of GC | 1084 GC |
Wu et al[52], 2021 | Prospective | WLI | Detection of Blind spotsAnd early gastric cancer | 1050 patients |
Ueyama et al[59], 2021 | Retrospective | ME-NBI | Differentiation of cancerous areas from non-cancerous areas | 2300 images |
Ling et al[48], 2021 | Retrospective | ME-NBI | Differentiation status and margins for EGC | 139 + 58 + 87 EGCs |
Ikenoyama et al[46], 2021 | Retrospective | WLI, CE, NBI | Detection of cancer | 140 lesions |
Hu et al[68], 2021 | Retrospective | ME-NBI | Detection of cancer | 295 lesions |
Oura et al[60], 2021 | Retrospective | WLI | Missing GC and point out low-quality images | 855 lesions + 50 lesions |
Zhang et al[61], 2021 | Retrospective | WLI | Detection of cancer | 1091 images |
Wu et al[51], 2021 | Prospective | WLI | Screening gastric lesions | 10000 patients |
Hamada et al[69], 2022 | Retrospective | WLI, CE, BLI | Depth of invasion of EGC | 68 patients |
Nam et al[47], 2022 | Retrospective | WLI | Lesion detection, differentiation and depth | 1366 patients |
Wu et al[49], 2022 | Prospective | ME-NBI | GC and EGC detection, EGC invasion depth and differentiation status |
- Citation: Panarese A. Usefulness of artificial intelligence in early gastric cancer. Artif Intell Cancer 2022; 3(2): 17-26
- URL: https://www.wjgnet.com/2644-3228/full/v3/i2/17.htm
- DOI: https://dx.doi.org/10.35713/aic.v3.i2.17