Table 1. The basic characteristics of the included literature.
| Author | Year | Region | Field focused | Method of study | Types of AI systems | Type of lesions | Type of images | Testing objects |
|---|---|---|---|---|---|---|---|---|
| Liu WN (9) | 2020 | China | Detection | Real-time use | 3D-CNN | Polyps of any size | NA | AI system |
| Misawa (12) | 2021 | Japan | Detection | Videos verification | YoloV3 | Polyps of any size | WLI | AI system |
| Urban (13) | 2018 | USA | Detection | Images and videos verification | DCNN | Polyps of any size | NA | AI system |
| Qadir (20) | 2021 | Norway | Detection | Image verification | F-CNN | Polyps of any size | NA | AI system |
| Guo (21) | 2021 | Japan | Detection | Videos verification | YoloV3 | Polyps of any size | NA | AI system/expert/nonexpert |
| Jia (22) | 2020 | Hong Kong, China | Detection | Image verification | CNN | Polyps of any size | NA | AI system |
| Liu P (23) | 2020 | China | Detection | Real-time use | Deep learning | Polyps of any size | NA | AI system |
| Poon (24) | 2020 | Hong Kong, China | Detection | Images and videos verification | CNN | Polyps of any size | NA | AI system |
| Shin (25) | 2018 | Norway | Detection | Images verification | Dictionary learning scheme | Polyps of any size | NA | AI system |
| Su (26) | 2020 | China | Detection | Real-time use | DCNN | Polyps of any size | NA | AI system |
| Wang (27) | 2019 | China | Detection | Real-time use | DCNN | Polyps of any size | NA | AI system |
| Wang (28) | 2020 | China | Detection | Real-time use | Deep learning | Polyps of any size | NA | AI system |
| Wang (29) | 2018 | China | Detection | Image and video verification | Deep learning | Polyps of any size | NA | AI system |
| Yu (30) | 2017 | Hong Kong, China | Detection | Image verification | 3D-FCN | Polyps of any size | NA | AI system |
| Zhang (31) | 2018 | Hong Kong, China | Detection | Images verification | DCNN | Polyps of any size | NA | AI system |
| Byrne (32) | 2019 | Canada | Classification | Video verification | DCNN | Polyps that ≤5 mm | NA | AI system |
| Chen (33) | 2018 | Taiwan, China | Classification | Video verification | DCNN | Polyps that ≤5 mm | NA | AI system/expert/nonexpert |
| Kominami (34) | 2016 | Japan | Classification | Image verification | SVM | Polyps of any size | NA | AI system |
| Kudo (35) | 2020 | Japan | Classification | Image verification | NA | Polyps that ≤10 mm | WLI/EC NBI/EC methylene blue staining | AI system/expert/nonexpert |
| Mori (36) | 2016 | Japan | Classification | Image verification | SVM | Polyps of any size | EC images | AI system/expert/nonexpert |
| Mori (37) | 2018 | Japan | Classification | Real-time use | NA | Polyps that ≤5 mm | EC NBI/EC methylene blue staining | AI system/expert/nonexpert |
| Mori (38) | 2015 | Japan | Classification | Image verification | NA | Polyps that ≤10 mm | WLI/EC images | AI system/expert/nonexpert |
| Patel (39) | 2020 | America | Classification | video verification | CNN | Polyps of any size | NA | AI system |
| Renner (40) | 2018 | Germany | Classification | Image verification | DCNN | Polyps of any size | NA | AI system/expert |
| Yamada (41) | 2019 | Japan | Classification | Image verification | NA | Polyps of any size | NA | AI system |
| Ozawa (42) | 2020 | Japan | Detection and Classification | Image verification | CNN | Polyps of any size | NA | AI system |
DCNN, deep convolutional neural network; CNN, convolutional neural network; YoloV3, a deep learning–based common object detection algorithm; NBI, narrow band imaging; 3D-FCN, three-dimensional fully convolutional network; F-CNN, fully convolutional neural network; 3D-CNN, three-dimensional convolutional neural network; SVM, support vector machine; WLI, white light imaging; EC, endocytoscopy; NA, not available.