Title: Identification of Barrett's Esophagus in Endoscopic Images Using Deep Learning
Version: 0 Date: 06 Oct 2021
Reviewer's report:
I read with interest the manuscript by Wen Pan and coworkers, about the development of a
deep learning method to identify Barrett's esophagus in endoscopic images. The authors reported
encouraging findings by using two FCN (for GEJ and SCJ, respectively).
The article is overall well written, with clear material and method, and reasonable conclusions.
However, there are some concerns the authors should deal with:
Introduction, line 4: "BE is the only known histological precursor of esophageal adenocarcinoma
(EAC) associated with high mortality (5-year survival rate <20%) and increasing incidences
[2-5]. "   BE seems to be the subject of the references while they are referred to esophageal
cancer. Please refrase.
Statistic: "IOU", It should be explicated at least once in the test.
Results - Patient characteristics and images:  this paragraph may fit better under Material and
Method section, as these are not actually findings and just replicate what already stated in M&M
section.
The authors correctly stated that BE is one of the most important risk factor for esophageal
adenocarcinoma. However, the risk of cancer is also related to BE extension. The authors should
provide data about performance of the DL according to the Prague Classification. (for example:
C3M3 vs C2M5 vs C0M1..)
Moreover, since the system is based on pictures, how dose the FCN performed for very long
barret? I believe is it difficult to have a single image of good quality of a BE>7 cm.
Could the algorithm work also on pictures obtained from different endoscope brand ? (i.e. Pentax
or Fuji)
Title: Identification of Barrett's Esophagus in Endoscopic Images Using Deep Learning
Version: 0 Date: 06 Oct 2021
Reviewer's report:
I read with interest the manuscript by Wen Pan and coworkers, about the development of a
deep learning method to identify Barrett's esophagus in endoscopic images. The authors reported
encouraging findings by using two FCN (for GEJ and SCJ, respectively).
The article is overall well written, with clear material and method, and reasonable conclusions.
However, there are some concerns the authors should deal with:
Introduction, line 4: "BE is the only known histological precursor of esophageal adenocarcinoma
(EAC) associated with high mortality (5-year survival rate <20%) and increasing incidences
[2-5]. "   BE seems to be the subject of the references while they are referred to esophageal
cancer. Please refrase.
Statistic: "IOU", It should be explicated at least once in the test.
Results - Patient characteristics and images:  this paragraph may fit better under Material and
Method section, as these are not actually findings and just replicate what already stated in M&M
section.
The authors correctly stated that BE is one of the most important risk factor for esophageal
adenocarcinoma. However, the risk of cancer is also related to BE extension. The authors should
provide data about performance of the DL according to the Prague Classification. (for example:
C3M3 vs C2M5 vs C0M1..)
Moreover, since the system is based on pictures, how dose the FCN performed for very long
barret? I believe is it difficult to have a single image of good quality of a BE>7 cm.
Could the algorithm work also on pictures obtained from different endoscope brand ? (i.e. Pentax
or Fuji)
Please review this paper.
user
Title: Identification of Barrett's Esophagus in Endoscopic Images Using Deep Learning
Version: 0 Date: 06 Oct 2021
Reviewer's report:
I read with interest the manuscript by Wen Pan and coworkers, about the development of a
deep learning method to identify Barrett's esophagus in endoscopic images. The authors reported
encouraging findings by using two FCN (for GEJ and SCJ, respectively).
The article is overall well written, with clear material and method, and reasonable conclusions.
However, there are some concerns the authors should deal with:
Introduction, line 4: "BE is the only known histological precursor of esophageal adenocarcinoma
(EAC) associated with high mortality (5-year survival rate <20%) and increasing incidences
[2-5]. "   BE seems to be the subject of the references while they are referred to esophageal
cancer. Please refrase.
Statistic: "IOU", It should be explicated at least once in the test.
Results - Patient characteristics and images:  this paragraph may fit better under Material and
Method section, as these are not actually findings and just replicate what already stated in M&M
section.
The authors correctly stated that BE is one of the most important risk factor for esophageal
adenocarcinoma. However, the risk of cancer is also related to BE extension. The authors should
provide data about performance of the DL according to the Prague Classification. (for example:
C3M3 vs C2M5 vs C0M1..)
Moreover, since the system is based on pictures, how dose the FCN performed for very long
barret? I believe is it difficult to have a single image of good quality of a BE>7 cm.
Could the algorithm work also on pictures obtained from different endoscope brand ? (i.e. Pentax
or Fuji)
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