Title: Reading of Small Bowel Capsule Endoscopy After Frame Reduction Using an Artificial
Intelligence Algorithm
Version: 2 Date: 01 Sep 2023
Reviewer's report:
The authors proposed an artificial intelligence algorithm for frame reduction of Small Bowel
Capsule Endoscopy Images to help doctors complete diagnostic tasks. And experiment showed
that the proposed AI algorithm can remove the image of poor visualization in SBCE and achieve
competitive results compared to AI non-user groups. However, there are some issues that need
to be addressed before publication.
(1) Which AI algorithm did the authors use? And how to train the AI algorithm?
(2) What is the motivation of the study?
(3) In the introduction, please add some recent literature. For example:
[1]. Gong E, Bang C, Lee J, et al.: Deep-learning-based clinical decision support system for
gastric neoplasms in real-time endoscopy: Development and validation study. Endoscopy AAM
2023.
[2]. Tang S, Yu X, Cheang C, et al.: Transformer-based multi-task learning for classification and
segmentation of gastrointestinal tract endoscopic images: Computers in Biology and Medicine
2023 (157): 106723.
[3]. Du H, Dong Z, Wu L, et al.: A deep-learning based system using multi-modal data for
diagnosing gastric neoplasms in real-time (with video). Gastric Cancer 2023 26 (2): 275-285.
(4) The authors should compare the proposed AI algorithm with other algorithms.
(5) The authors should analyze the experiment results in detail. In fact, it is difficult to understand
why the proposed method is a good one.
(6) English writing should be improved.
Title: Reading of Small Bowel Capsule Endoscopy After Frame Reduction Using an Artificial
Intelligence Algorithm
Version: 2 Date: 01 Sep 2023
Reviewer's report:
The authors proposed an artificial intelligence algorithm for frame reduction of Small Bowel
Capsule Endoscopy Images to help doctors complete diagnostic tasks. And experiment showed
that the proposed AI algorithm can remove the image of poor visualization in SBCE and achieve
competitive results compared to AI non-user groups. However, there are some issues that need
to be addressed before publication.
(1) Which AI algorithm did the authors use? And how to train the AI algorithm?
(2) What is the motivation of the study?
(3) In the introduction, please add some recent literature. For example:
[1]. Gong E, Bang C, Lee J, et al.: Deep-learning-based clinical decision support system for
gastric neoplasms in real-time endoscopy: Development and validation study. Endoscopy AAM
2023.
[2]. Tang S, Yu X, Cheang C, et al.: Transformer-based multi-task learning for classification and
segmentation of gastrointestinal tract endoscopic images: Computers in Biology and Medicine
2023 (157): 106723.
[3]. Du H, Dong Z, Wu L, et al.: A deep-learning based system using multi-modal data for
diagnosing gastric neoplasms in real-time (with video). Gastric Cancer 2023 26 (2): 275-285.
(4) The authors should compare the proposed AI algorithm with other algorithms.
(5) The authors should analyze the experiment results in detail. In fact, it is difficult to understand
why the proposed method is a good one.
(6) English writing should be improved.
Please review this paper.
user
Title: Reading of Small Bowel Capsule Endoscopy After Frame Reduction Using an Artificial
Intelligence Algorithm
Version: 2 Date: 01 Sep 2023
Reviewer's report:
The authors proposed an artificial intelligence algorithm for frame reduction of Small Bowel
Capsule Endoscopy Images to help doctors complete diagnostic tasks. And experiment showed
that the proposed AI algorithm can remove the image of poor visualization in SBCE and achieve
competitive results compared to AI non-user groups. However, there are some issues that need
to be addressed before publication.
(1) Which AI algorithm did the authors use? And how to train the AI algorithm?
(2) What is the motivation of the study?
(3) In the introduction, please add some recent literature. For example:
[1]. Gong E, Bang C, Lee J, et al.: Deep-learning-based clinical decision support system for
gastric neoplasms in real-time endoscopy: Development and validation study. Endoscopy AAM
2023.
[2]. Tang S, Yu X, Cheang C, et al.: Transformer-based multi-task learning for classification and
segmentation of gastrointestinal tract endoscopic images: Computers in Biology and Medicine
2023 (157): 106723.
[3]. Du H, Dong Z, Wu L, et al.: A deep-learning based system using multi-modal data for
diagnosing gastric neoplasms in real-time (with video). Gastric Cancer 2023 26 (2): 275-285.
(4) The authors should compare the proposed AI algorithm with other algorithms.
(5) The authors should analyze the experiment results in detail. In fact, it is difficult to understand
why the proposed method is a good one.
(6) English writing should be improved.
assistant