Minireviews
Copyright ©The Author(s) 2020.
Artif Intell Gastroenterol. Nov 28, 2020; 1(4): 71-85
Published online Nov 28, 2020. doi: 10.35712/aig.v1.i4.71
Table 1 Previous studies on upper endoscopy of gastric cancer using artificial intelligence
Ref.
Targets
Sample sizes
Inputs
Tasks
Analysis method
Diagnostic performance
Yoon et al[28] GC (ESD/surgery)800 casesGC/non-GC images in close-up and distant viewsDetection and invasion depth predictionCNNAUC: detection, 0.981; depth, 0.851
Zhu et al[29] GC 993 imagesGC imagesDiagnosis of invasion depthCNNSensitivity: 76.4%, PPV: 89.6%
Li et al[30] GC and healthy 386 GC and 1702 NC imagesNBI imagesDiagnosis of GCCNNSensitivity: 91.1%, PPV: 90.6%
Hirasawa et al[31] GC13584 training and 2296 test imagesGC imagesDiagnosis of GCCNNSensitivity: 92.2%, PPV: 30.6%
Ishioka et al[32] EGC 62 casesReal-time imagesDetectionCNNDetection rate: 94.1%
Luo et al[33] GC 1036496 imagesGC imagesDetectionCNNPPV: 0.814, NPV:0.978
Horiuchi et al[34] GC and gastritis 1492 GC and 1078 gastritis imagesNBI imagesDetectionCNNSensitivity: 95.4%, PPV: 82.3%