Title: Deep Learning Model Can Improve the Diagnosis Rate of Endoscopic Chronic Atrophic
Gastritis: A Prospective Cohort Study
Version: 2 Date: 26 Jan 2022
Reviewer's report:
Quchuan Zhao's manuscript aims to demonstrate the contribution of deep learning to the
recognition of gastric glandular atrophy lesions.
The model was trained on a database of 5290 images from 1711 patients.  In this database, 4175
images concerned glandular atrophy of different grades of severity. The manuscript was evasive
on the histopathological evaluation of glandular atrophy, although this is the only determinant
factor independent of the observer. It would have been mandatory to train the model from
photographed sites where oriented biopsies would performed. Only the analysis of which would
have determined the nature of the lesions to train the deep learning network.
Without this feature, the reader has the impression that the deep learning network was trained
to recognise the lesions identified by the endoscopists of the centre.
This negative impression is even more pronounced as the diagnostic performance of deep
learning has not been compared with that of endoscopists from other centres.
Title: Deep Learning Model Can Improve the Diagnosis Rate of Endoscopic Chronic Atrophic
Gastritis: A Prospective Cohort Study
Version: 2 Date: 26 Jan 2022
Reviewer's report:
Quchuan Zhao's manuscript aims to demonstrate the contribution of deep learning to the
recognition of gastric glandular atrophy lesions.
The model was trained on a database of 5290 images from 1711 patients.  In this database, 4175
images concerned glandular atrophy of different grades of severity. The manuscript was evasive
on the histopathological evaluation of glandular atrophy, although this is the only determinant
factor independent of the observer. It would have been mandatory to train the model from
photographed sites where oriented biopsies would performed. Only the analysis of which would
have determined the nature of the lesions to train the deep learning network.
Without this feature, the reader has the impression that the deep learning network was trained
to recognise the lesions identified by the endoscopists of the centre.
This negative impression is even more pronounced as the diagnostic performance of deep
learning has not been compared with that of endoscopists from other centres.
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: 26 Jan 2022
Reviewer's report:
Quchuan Zhao's manuscript aims to demonstrate the contribution of deep learning to the
recognition of gastric glandular atrophy lesions.
The model was trained on a database of 5290 images from 1711 patients.  In this database, 4175
images concerned glandular atrophy of different grades of severity. The manuscript was evasive
on the histopathological evaluation of glandular atrophy, although this is the only determinant
factor independent of the observer. It would have been mandatory to train the model from
photographed sites where oriented biopsies would performed. Only the analysis of which would
have determined the nature of the lesions to train the deep learning network.
Without this feature, the reader has the impression that the deep learning network was trained
to recognise the lesions identified by the endoscopists of the centre.
This negative impression is even more pronounced as the diagnostic performance of deep
learning has not been compared with that of endoscopists from other centres.
assistant