Title: Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis:
a prospective nested case-control study
Version: 1 Date: 31 May 2022
Reviewer's report:
Reviewer's report
Title: Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis:
a prospective nested case-control study
Reviewer: Guoping Liu
Reviewer's report:
General comments:
This is an interesting work on the study of the endoscopic diagnosis in CAG based on U-Net deep
learning (DL), and the paper developed a real-time video monitoring model for endoscopic and
conducted a prospective nested case-control study to evaluate the diagnostic evaluation indices
of the model and its consistency with pathological diagnosis. The results of the study proved
that the real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net DL
were superior to those of endoscopists.
Major Compulsory Revisions:
1.This study protocol was approved by the ethics committee of Xuanwu Hospital of Capital
Medical University, but Ethics committee approval number is not indicated in the paper. it
should be added.
2.The paper is the intersection study of computing and medicine. The author uses computer
methods of deep learning to solve medical problems, but the study lacks the participation of
persons or teams with computer background. How deep learning and U-Net are combined to
build the real-time video monitoring model should be supplemented.
3.The actual contribution of the author should be clearly written in the part of authors’
contributions, such as endoscopy image organization, model building, etc.
Level of interest: An article whose findings are important to those with closely
related research interests
Quality of written English: Not needs some language corrections before being Published On
the whole.
Declaration of competing interests:
I declare that I have no competing interests
Title: Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis:
a prospective nested case-control study
Version: 1 Date: 31 May 2022
Reviewer's report:
Reviewer's report
Title: Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis:
a prospective nested case-control study
Reviewer: Guoping Liu
Reviewer's report:
General comments:
This is an interesting work on the study of the endoscopic diagnosis in CAG based on U-Net deep
learning (DL), and the paper developed a real-time video monitoring model for endoscopic and
conducted a prospective nested case-control study to evaluate the diagnostic evaluation indices
of the model and its consistency with pathological diagnosis. The results of the study proved
that the real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net DL
were superior to those of endoscopists.
Major Compulsory Revisions:
1.This study protocol was approved by the ethics committee of Xuanwu Hospital of Capital
Medical University, but Ethics committee approval number is not indicated in the paper. it
should be added.
2.The paper is the intersection study of computing and medicine. The author uses computer
methods of deep learning to solve medical problems, but the study lacks the participation of
persons or teams with computer background. How deep learning and U-Net are combined to
build the real-time video monitoring model should be supplemented.
3.The actual contribution of the author should be clearly written in the part of authors’
contributions, such as endoscopy image organization, model building, etc.
Level of interest: An article whose findings are important to those with closely
related research interests
Quality of written English: Not needs some language corrections before being Published On
the whole.
Declaration of competing interests:
I declare that I have no competing interests
Please review this paper.
user
Title: Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis:
a prospective nested case-control study
Version: 1 Date: 31 May 2022
Reviewer's report:
Reviewer's report
Title: Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis:
a prospective nested case-control study
Reviewer: Guoping Liu
Reviewer's report:
General comments:
This is an interesting work on the study of the endoscopic diagnosis in CAG based on U-Net deep
learning (DL), and the paper developed a real-time video monitoring model for endoscopic and
conducted a prospective nested case-control study to evaluate the diagnostic evaluation indices
of the model and its consistency with pathological diagnosis. The results of the study proved
that the real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net DL
were superior to those of endoscopists.
Major Compulsory Revisions:
1.This study protocol was approved by the ethics committee of Xuanwu Hospital of Capital
Medical University, but Ethics committee approval number is not indicated in the paper. it
should be added.
2.The paper is the intersection study of computing and medicine. The author uses computer
methods of deep learning to solve medical problems, but the study lacks the participation of
persons or teams with computer background. How deep learning and U-Net are combined to
build the real-time video monitoring model should be supplemented.
3.The actual contribution of the author should be clearly written in the part of authors’
contributions, such as endoscopy image organization, model building, etc.
Level of interest: An article whose findings are important to those with closely
related research interests
Quality of written English: Not needs some language corrections before being Published On
the whole.
Declaration of competing interests:
I declare that I have no competing interests
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