Title: Deep Learning Model Can Improve the Diagnosis Rate of Endoscopic Chronic Atrophic
Gastritis: A Prospective Cohort Study
Version: 2 Date: 15 Jan 2022
Reviewer's report:
I read with interest the study by  Quchuan Zhao and  Tianyu Chi entitled "Deep Learning Model
Can Improve the Diagnosis Rate of Endoscopic Chronic Atrophic Gastritis: A Prospective
Cohort Study". I think that the study is well written with a solid background, exhaustively
described methods,  robust statistical methods and well-presented results with efficient tables
and figures.
What emerges from this paper is that this tool may  improve the diagnostic performance of
endoscopists and this will  ultimately be of benefit for patients.
Ihave only  a few comments:
One is  on the "Deep learning model training and testing" described in the Methods section. I
wonder whether these data on the U-NET network used to build the real-time video monitoring
diagnostic model for endoscopic CAG based on DL used in this study was previously published
as no reference is stated.
I would also suggest to include in the Discussion section that endoscopical diagnosis of CAG
is  a skill much more diffuse in Asian countries, as in Western countries diagnosi of CAG still
relies  mainly on pathology.
In  the Methods section I did not read the type of endoscopes used,  did the authors use
magnification, high-resolution or chromoendoscopy techniques as BLI or NBI.
Title: Deep Learning Model Can Improve the Diagnosis Rate of Endoscopic Chronic Atrophic
Gastritis: A Prospective Cohort Study
Version: 2 Date: 15 Jan 2022
Reviewer's report:
I read with interest the study by  Quchuan Zhao and  Tianyu Chi entitled "Deep Learning Model
Can Improve the Diagnosis Rate of Endoscopic Chronic Atrophic Gastritis: A Prospective
Cohort Study". I think that the study is well written with a solid background, exhaustively
described methods,  robust statistical methods and well-presented results with efficient tables
and figures.
What emerges from this paper is that this tool may  improve the diagnostic performance of
endoscopists and this will  ultimately be of benefit for patients.
Ihave only  a few comments:
One is  on the "Deep learning model training and testing" described in the Methods section. I
wonder whether these data on the U-NET network used to build the real-time video monitoring
diagnostic model for endoscopic CAG based on DL used in this study was previously published
as no reference is stated.
I would also suggest to include in the Discussion section that endoscopical diagnosis of CAG
is  a skill much more diffuse in Asian countries, as in Western countries diagnosi of CAG still
relies  mainly on pathology.
In  the Methods section I did not read the type of endoscopes used,  did the authors use
magnification, high-resolution or chromoendoscopy techniques as BLI or NBI.
Please review this paper.
user
Title: Deep Learning Model Can Improve the Diagnosis Rate of Endoscopic Chronic Atrophic
Gastritis: A Prospective Cohort Study
Version: 2 Date: 15 Jan 2022
Reviewer's report:
I read with interest the study by  Quchuan Zhao and  Tianyu Chi entitled "Deep Learning Model
Can Improve the Diagnosis Rate of Endoscopic Chronic Atrophic Gastritis: A Prospective
Cohort Study". I think that the study is well written with a solid background, exhaustively
described methods,  robust statistical methods and well-presented results with efficient tables
and figures.
What emerges from this paper is that this tool may  improve the diagnostic performance of
endoscopists and this will  ultimately be of benefit for patients.
Ihave only  a few comments:
One is  on the "Deep learning model training and testing" described in the Methods section. I
wonder whether these data on the U-NET network used to build the real-time video monitoring
diagnostic model for endoscopic CAG based on DL used in this study was previously published
as no reference is stated.
I would also suggest to include in the Discussion section that endoscopical diagnosis of CAG
is  a skill much more diffuse in Asian countries, as in Western countries diagnosi of CAG still
relies  mainly on pathology.
In  the Methods section I did not read the type of endoscopes used,  did the authors use
magnification, high-resolution or chromoendoscopy techniques as BLI or NBI.
assistant