Usefulness of artificial intelligence in gastric neoplasms

Table 1 Recently published articles on application of artificial intelligence in gastric neoplasms

Ref.	Purpose	AI type	Endoscopy type	Subjects	Outcomes
Detection of gastric neoplasms
Hirasawa et al[21], 2018	Detect EGC	CNN (SSD)	Conventional endoscopy	Training: 13584 images; Test: 2296 images from 69 patients.	Sensitivity 92.2%, PPV 30.6%
Ishioka et al[22], 2019	Real time detection of EGC	CNN (SSD)	Conventional endoscopy	Live video of 62 patients	Accuracy 94.1%, median time 1 s (range: 0-44 s)
Sakai et al[23], 2018	Detect EGC	CNN	Conventional endoscopy	Training: 348943 images; Test: 9650 images	Accuracy 82.8%
Kanesaka et al[24], 2018	Detect EGC	SVM	M-NBI	Training: 126 images; Test: 81 images	Accuracy 96.3%, sensitivity 96.7%, specificity 95%
Li et al[25], 2020	Detect EGC	CNN (Inception-v3)	M-NBI	Training: 2088 images; Test: 341 images	Accuracy 91.2%, sensitivity 90.6%, specificity 90.9%
Horiuchi et al[26], 2020	Classifying EGC from gastritis	CNN (GoogLeNet)	M-NBI	Training: 2570 images; Test: 258 images.	Accuracy 85.3%, sensitivity 95.4%, specificity 71.0%, test speed 51.83 images/s (0.02 s/image)
Horiuchi et al[27], 2020	Detect EGC	CNN (GoogLeNet)	M-NBI	174 videos	Accuracy 85.1%, AUC 0.8684, sensitivity 87.4%, specificity 82.8%, PPV 83.5%, NPV 86.7%
Luo et al[28], 2019	Real time detection of EGC	GRAIDS	Conventional endoscopy	1036496 images from 84424 patients	Sensitivity (0.942) similar to the expert (0.945), superior to the competent (0.858) and the trainee (0.722) endoscopist
Ikenoyama et al[29], 2021	Detect EGC	CNN (SSD)	WLI, NBI chromoendoscopy	Training: 13584 images; Test: 2940 images.	Sensitivity 58.4%, specificity 87.3%, PPV 26.0%, NPV 96.5%
Classification of gastric neoplasms
Sun et al[30], 2018	Classify ulcers	DCNN	Conventional endoscopy	854 images	Accuracy 86.6%, sensitivity 90.8%, specificity 83.5%
Lee et al[31], 2019	Detect EGC and benign ulcer	CNN (ResNet50, Inception-v3, VGG16)	Conventional endoscopy	Training: 717 images; Test: 70 images	AUC 0.95, 0.97, and 0.85 in Inception, ResNet50, and VGG16
Cho et al[32], 2019	Detect AGC, EGC, dysplasia	CNN (Inception-v4, ResNet152, Inception-Resnet-v2)	Conventional endoscopy	5217 images from 1469 patients	Gastric cancer: accuracy 81.9%, AUC 0.877; Gastric neoplasm: accuracy 85.5%, AUC 0.927
Kim et al[33], 2020	Classify gastric mesenchymal tumors	CNN	Endoscopic ultrasonography	Training: 905 images; Test: 212 images.	Accuracy 79.2%, sensitivity 83.0%. specificity 75.5%
Prediction of invasion depth
Kubota et al[39], 2012	Predict invasion depth	Back propagation	Conventional endoscopy	Training: 800 images; Test: 90 images	Accuracy 77.9%, 29.1%, 51.0% and 55.3% in T1, T2, T3, and T4 stage; Accuracy 68.9% and 63.6% in T1a and T1b stage
Zhu et al[40], 2019	Predict invasion depth	CNN (ResNet50)	Conventional endoscopy	Training: 790 images; Test: 203 images	AUC 0.94, overall accuracy 89.2%, sensitivity 76.5%, specificity 95.6%
Yoon et al[41], 2019	Detect cancer, and predict invasion depth	CNN (VGG16, Grad-CAM)	Conventional endoscopy	11539 images	Detection AUC 0.981, depth prediction AUC 0.851 (undifferentiated type histology with a lower accuracy)
Cho et al[43], 2020	Predict invasion depth	CNN (Inception-ResNet-v2, DenseNet-161)	Conventional endoscopy	Training: 2899 images, test: 206 images	Internal validation: accuracy 84.1%, AUC 0.887; External validation: accuracy 77.3%, AUC 0.887
Nagao et al[44], 2020	Predict invasion depth	CNN (ResNet50)	WLI, NBI, indigo-carmine	16557 images from 1084 cases of gastric cancer	WLI: AUC 0.9590, sensitivity 89.2%, specificity 98.7%, accuracy 94.4%, PPV 98.3%, NPV 91.7%; NBI: AUC 0.9048; Indigo-carmine: AUC 0.9191
Blind-spot monitoring
Wu et al[48], 2019	Detect blind spot	DCNN	Conventional endoscopy	34513 images	Accuracy of detecting blind spot: 90.0%; Blind spot rate: 5.9%
Wu et al[49], 2019	Detect EGC and blind spot	DCNN	Conventional endoscopy	24549 images	Accuracy 92.5%, sensitivity 94.0%, specificity 91.0%, PPV 91.3%, NPV 93.8%
Chen et al[50], 2020	Detect blind spot	DCNN	Conventional endoscopy, U-TOE	Live video of 437 patients	Blind spot rate with AI: Sedated C-EGD, 3.4%; unsedated U-TOE, 21.8%; unsedated C- EGD, 31.2%

AI: Artificial intelligence; EGC: Early gastric cancer; CNN: Convolutional neural network; SSD: Single Shot MultiBox Detector; PPV: Positive predict value; SVM: Support vector machine; M-NBI: Magnified narrow-band imaging; AUC: Area under curve; GRAIDS: Gastrointestinal Artificial Intelligence Diagnostic System; WLI: White light imaging; NPV: Negative predict value; DCNN: Deep convolutional neural network; VGG: Visual Geometry Group; AGC: Advanced gastric cancer; Grad-CAM: Gradient-weighted class activation mapping; U-TOE: ultrathin transoral endoscopy; C-EGD: conventional esophagogastroduodenoscopy.