Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions

Table 6 Summary of retrospective and prospective studies on computer aided detection for detecting colorectal polyps[150,222-229]

Ref.	Study design	Number	Country	Outcomes
Park et al[222]	Retrospective ex vivo	562 images	United States	Se: 86%; Sp: 85%
Billah et al[223]	Retrospective ex vivo	14000 images	Bangladesh	Se: 99%; Sp: 99%
Lequan et al[224]	Retrospective ex vivo	38 videos	China	Se: 71%; PPV: 88%
Zhang et al[225]	Retrospective ex vivo	2442 images	China	Se: 98%; PPV: 99%; Acc: 86%
Misawa et al[226]	Retrospective ex vivo	546 videos	Japan	Per-frame Se: 90%; Sp: 63%; Acc: 76%; Per-polyp Se: 94%
Urban et al[150]	Retrospective ex vivo	63559 images, 40 videos	United States	Se: 90%; Acc: 96%
Yamada et al[227]	Retrospective ex vivo	144823 video images	Japan	Se: 97%; Sp: 99%
Klare et al[228]	Prospective in vivo	55 colonoscopies	Germany	Per-polyp Se: 75%; ADR: 29%; PDR: 51% (31% and 56% in endoscopist)
Ozawa et al[229]	Ex vivo	23495 images	Japan	Se: 92%; PPV: 93%; Acc: 85%

Acc: Accuracy; Se: Sensitivity; Sp: Specificity; PPV: Positive predictive value; ADR: Adenoma detection rate; PDR: Polyp detection rate.