Utility of artificial intelligence in colonoscopy

Table 1 List of studies evaluating role of artificial intelligence in the detection of colon polyps during the colonoscopy

Ref.	Country of origin	Study design	Results
Fernandez-Esparrach et al[13], 2016	Spain	Retrospective	Sensitivity 70%, Specificity 72 %
Geetha et al[36], 2016	India	Retrospective	Sensitivity 95%, Specificity 97%
Misawa et al[37], 2017	Japan	Retrospective	Accuracy higher than trainees (87.8 vs 63.4%; P = 0.01), but similar to experts (87.8 vs 84.2%; P = 0.76)
Zhang et al[38], 2017	China	Retrospective	Accuracy 86%
Yu et al[39], 2017	China	Retrospective	Sensitivity 71%, PPV 88%
Billah et al[40], 2017	Bangladesh	Retrospective	Sensitivity 99%, Specificity 98.5%, Accuracy 99%
Chen et al[23], 2018	Taiwan	Retrospective	Sensitivity 96.3%, Specificity 78.1%
Urban et al[18], 2018	United States	Retrospective	Accuracy 96.4%
Misawa et al[17], 2018	Japan	Retrospective	Sensitivity, Specificity, and Accuracy were 90%, 63%, and 76%, respectively
Wang et al[19], 2018	China	Retrospective	Sensitivity 94.38%, Specificity 95.92%
Su et al[41], 2019	China	Prospective	Polyp detection rate was 38.3% as compared to 25.4% in control group (P < 0.001)
Wang et al[42], 2019	China	Prospective	Polyp detection rate was 45% as compared to 29% in the control group (P < 0.001)
Klare et al[43], 2019	Germany	Prospective	Larger polyp detection, Odds ration 2.71, P = 0.042
Figueiredo et al[44], 2019	Portugal	Retrospective	Sensitivity 99.7%, Specificity 84.9%, Accuracy 91.1%
Yamada et al[45], 2019	Japan	Retrospective	Sensitivity 97.3%, Specificity: 99%
Lee[46], 2020	South Korea	Retrospective	Accuracy 93.4%, Sensitivity 89.9%, Specificity 93.7%
Luo et al[16], 2020	China	Prospective	Polyp detection rate for diminutive polyps increased (38.7% vs 34%, P < 0.001). No difference was found for larger polyps
Gong[47], 2020	China	Prospective	Polyp detection rate was 47% as compared to 34% in control group (P = 0.0016)
Liu et al[48], 2020	China	Prospective	Polyp detection rate was 44% as compared to 28% in control group (P < 0.001)
Ozawa et al[49], 2020	Japan	Retrospective	Sensitivity 92%, PPV 86%, Accuracy 83%
Wang et al[50], 2020	China	Prospective	Polyp detection rate was 52% as compared to 37% in control group (P < 0.0001)
Hasssan et al[51], 2020	Italy	Retrospective	Sensitivity 99.7%
Repici et al[52], 2020	Italy	Prospective	Adenoma detection rate was 54.8% as compared to 40.4% in control group (P < 0.001)

PPV: Positive predictive value.

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.