Utility of artificial intelligence in colonoscopy

Table 2 List of studies evaluating role of artificial intelligence in characterization of colon polyps during the colonoscopy

Ref.	Country of origin	Study design	Results
Misawa et al[53], 2016	Japan	Retrospective	Sensitivity 84.5%, Specificity 98%
Mori et al[54], 2016	Japan	Retrospective	Accuracy 89%
Kominami et al[55], 2016	Japan	Prospective	Sensitivity 93%, Specificity 93.3%
Komeda et al[56], 2017	Japan	Retrospective	Accuracy 75%
Takeda et al[57], 2017	Japan	Retrospective	Sensitivity 89.4%, Specificity 98.9%, Accuracy 94.1 %
Chen et al[23], 2018	Taiwan	Retrospective	PPV of 89.6%, and a NPV of 91.5%
Renner[58], 2018	Germany	Retrospective	Sensitivity 92.3% and NPV 88.2%
Mori et al[59], 2018	Japan	Prospective	Accuracy 98.1%
Blanes-Vidal et al[60], 2019	Denmark	Retrospective	Accuracy 96.4%
Min et al[61], 2019	China	Prospective	Sensitivity 83.3%, Specificity 70.1%
Byrne [22], 2019	Canada	Retrospective	Accuracy 94%
Sánchez-Monteset al[62], 2019	Spain	Retrospective	Sensitivity 92.3%, Specificity 89.2%
Horiuchi et al[63], 2019	Japan	Prospective	Sensitivity 80%, Specificity 95.3%
Lui et al[64], 2019	China	Retrospective	Sensitivity 88.2%, Specificity 77.9%
Ozawa et al[49], 2020	Japan	Retrospective	Sensitivity 97%, PPV 84%, NPV 88%
Jin et al[65], 2020	South Korea	Prospective	Sensitivity 83.3%, Specificity 91.7%
Rodriguez-Diazet al[66], 2020	United States	Prospective	Sensitivity 96%, Specificity 84%
Kudo et al[67], 2020	Japan	Retrospective	Sensitivity 96.9%, Specificity 100%

NPV: Negative predictive value; PPV: Positive predictive value.