Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease

Table 2 Recent researches on artificial intelligence in benign digestive system diseases

Ref.	Study design	Study population	Year	Disease	Country/Region	Number of cases	Methods	Results
Maeda et al[35]	Retrospective	Hospital	2019	UC	Japan	12900 Images	CAD	Sensitivity: 74%; Specificity: 97%; Accuracy: 91%
Popa et al[56]	Retrospective	Hospital	2020	UC	Romania	55 Cases	ML	Accuracy: 90%; AUC: 0.92
Tong et al[57]	Retrospective	Hospital	2020	UC	China	6399 Cases	RF, CNN	Sensitivity: RF (0.89); Sensitivity: CNN (0.90)
Qiu et al[38]	Retrospective	Hospital	2019	SAP	China	263 Cases	ANN	Sensitivity: 80.99%; Specificity: 89.44%
Chen et al[42]	Retrospective	Hospital	2019	SAP	China	389 Cases	LR	Accuracy: 87.1%
Namikawa et al[45]	Retrospective	Hospital	2020	Gastric ulcer	Japan	720 Images	A-CNN	Sensitivity: 93.3%; Specificity: 99.0%; PPV: 99.1%
Steinbuss et al[46]	Retrospective	Hospital	2020	Gastritis	Germany	1230 Images	CNN	Accuracy: 84%; Sensitivity: 100%; Specificity: 93%
Ozawa et al[47]	Retrospective	Hospital	2020	Colorectal polyps	Japan	16418 Images	CNN	Sensitivity: 92%; PPV: 86%

UC: Ulcerative colitis; SAP: Severe acute pancreatitis; CAD: Computer-aided diagnosis; ML: Machine learning; RF: Random forest; CNN: Convolutional neural network; ANN: Artificial neural networks; LR: Logistic regression; A-CNN: Advanced convolutional neural network; AUC: Area under the curve; PPV: Positive predictive value.