Title: Identification of Barrett's Esophagus in Endoscopic Images Using Deep Learning
Version: 0 Date: 20 Sep 2021
Reviewer's report:
I read with great interest the paper by Wen Pan et al. They create a deep learning method to
identificate the GEJ and SCJ in Barrett’s esophagus diagnosed patients. AI applied at digestive
endoscopy evolved tremendously in the recent years.  In particular, many studies created DL
methods to predict early neoplasia in Barrett’s esophagus patients and/or submucosal invasion.
However few papers studied the diagnosis of Barrett esophagus with AI. A recent paper by
Sharib Ali et al (Gastroenterology 202110.1053/j.gastro.2021.05.059) was published recently
and showed a great accuracy for C&M Prague identification (please cite this paper in the
introduction).
The present paper showed a good and optimal accuracy for the segmentation of the GEJ and
SCJ using two separate FCN networks.
Although there are already data in literature about application of AI in Barrett esophagus, i think
that implementation of the literature with other data in this expanding field is helpful.
In order to have an application in clinical practice i suggest an expansion to a further project
that could be added in discussion: i think that a crucial point for BE patients is to discriminate
those that have actually an elevation of SCJ (more than 1 cm over the EGJ) than those who have
not. Endoscopists often overestimate the elevation of the SCJ with a wrong diagnosis of “short
Barrett” leading to subsequent useless follow-up endoscopies. I think that the application of AI
in order to exclude the elevation of SCJ more than 1 cm could be an application of an evolution
of the DL created by the Authors of the present paper. Moreover this type of DL could be easily
applicated in clinical practice.
Minor comment:
table 1 is too poor and needs to be implemented with more data of BE patients (i.e. length of BE)
Title: Identification of Barrett's Esophagus in Endoscopic Images Using Deep Learning
Version: 0 Date: 20 Sep 2021
Reviewer's report:
I read with great interest the paper by Wen Pan et al. They create a deep learning method to
identificate the GEJ and SCJ in Barrett’s esophagus diagnosed patients. AI applied at digestive
endoscopy evolved tremendously in the recent years.  In particular, many studies created DL
methods to predict early neoplasia in Barrett’s esophagus patients and/or submucosal invasion.
However few papers studied the diagnosis of Barrett esophagus with AI. A recent paper by
Sharib Ali et al (Gastroenterology 202110.1053/j.gastro.2021.05.059) was published recently
and showed a great accuracy for C&M Prague identification (please cite this paper in the
introduction).
The present paper showed a good and optimal accuracy for the segmentation of the GEJ and
SCJ using two separate FCN networks.
Although there are already data in literature about application of AI in Barrett esophagus, i think
that implementation of the literature with other data in this expanding field is helpful.
In order to have an application in clinical practice i suggest an expansion to a further project
that could be added in discussion: i think that a crucial point for BE patients is to discriminate
those that have actually an elevation of SCJ (more than 1 cm over the EGJ) than those who have
not. Endoscopists often overestimate the elevation of the SCJ with a wrong diagnosis of “short
Barrett” leading to subsequent useless follow-up endoscopies. I think that the application of AI
in order to exclude the elevation of SCJ more than 1 cm could be an application of an evolution
of the DL created by the Authors of the present paper. Moreover this type of DL could be easily
applicated in clinical practice.
Minor comment:
table 1 is too poor and needs to be implemented with more data of BE patients (i.e. length of BE)
Please review this paper.
user
Title: Identification of Barrett's Esophagus in Endoscopic Images Using Deep Learning
Version: 0 Date: 20 Sep 2021
Reviewer's report:
I read with great interest the paper by Wen Pan et al. They create a deep learning method to
identificate the GEJ and SCJ in Barrett’s esophagus diagnosed patients. AI applied at digestive
endoscopy evolved tremendously in the recent years.  In particular, many studies created DL
methods to predict early neoplasia in Barrett’s esophagus patients and/or submucosal invasion.
However few papers studied the diagnosis of Barrett esophagus with AI. A recent paper by
Sharib Ali et al (Gastroenterology 202110.1053/j.gastro.2021.05.059) was published recently
and showed a great accuracy for C&M Prague identification (please cite this paper in the
introduction).
The present paper showed a good and optimal accuracy for the segmentation of the GEJ and
SCJ using two separate FCN networks.
Although there are already data in literature about application of AI in Barrett esophagus, i think
that implementation of the literature with other data in this expanding field is helpful.
In order to have an application in clinical practice i suggest an expansion to a further project
that could be added in discussion: i think that a crucial point for BE patients is to discriminate
those that have actually an elevation of SCJ (more than 1 cm over the EGJ) than those who have
not. Endoscopists often overestimate the elevation of the SCJ with a wrong diagnosis of “short
Barrett” leading to subsequent useless follow-up endoscopies. I think that the application of AI
in order to exclude the elevation of SCJ more than 1 cm could be an application of an evolution
of the DL created by the Authors of the present paper. Moreover this type of DL could be easily
applicated in clinical practice.
Minor comment:
table 1 is too poor and needs to be implemented with more data of BE patients (i.e. length of BE)
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