Reviewer’s report
Title: 
Deep Learning for Sensitive Detection of Helicobacter Pylori in Gastric
Biopsies
Version: 
0 
Date: 
20 Jan 2020
Reviewer: 
Yu-Ching Tsai
Reviewer's report:
In the present study, the authors designed a deep-learning-based decision support
algorithm that could be applied to more sensitively detect the presence of H.
pylori on slides of gastric biopsies. It is interesting and could decrease the burden
of the pathologist. There are some comments about this manuscript:
1. The artificial intelligence algorithm designed by the authors had high
sensitivity and negative predictive value. Thus it would be a suitable tool for
screening and narrowing the scope of H. pylori detection. However, because its
specificity was 
lower than the microscopic studies by the pathologist. Thus it
might still need manual evaluations by the pathologists after positive H. pylori
infection "picked up" by the computer algorithm to achieve good accuracy for H.
pylori detection. 
2. The Figure 5 is not found in the PDF file of the manuscript.
3. The authors are suggested to clarify the meanings of the proportions in tables
in Fig. 3 and Fig. 4. What the numbers and the proportions stand for? For
example, in table of Fig. 3, in the column of Giemsa stain, 35/347=10% but
172/347= 50%, not 37%. Thus the data in the tables shall be reviewed and
corrected if any error.
Are the methods appropriate and well described?
If not, please specify what is required in your comments to the authors.
Yes
Does the work include the necessary controls?
If not, please specify which controls are required in your comments to the
authors.
Yes
Are the conclusions drawn adequately supported by the data shown?
If not, please explain in your comments to the authors.
Unable to assess
Are you able to assess any statistics in the manuscript or would you
recommend an additional statistical review?
If an additional statistical review is recommended, please specify what aspects
require further assessment in your comments to the editors.
I recommend additional statistical review
Quality of written English
Please indicate the quality of language in the manuscript:
Acceptable
Declaration of competing interests
Please complete a declaration of competing interests, considering the following
questions:
1
. 
Have you in the past five years received reimbursements, fees, funding, or
salary from an organisation that may in any way gain or lose financially
from the publication of this manuscript, either now or in the future?
2
. 
Do you hold any stocks or shares in an organisation that may in any way
gain or lose financially from the publication of this manuscript, either now
or in the future?
3
. 
Do you hold or are you currently applying for any patents relating to the
content of the manuscript?
4
. 
Have you received reimbursements, fees, funding, or salary from an
organization that holds or has applied for patents relating to the content of
the manuscript?
5
. 
Do you have any other financial competing interests?
6
. 
Do you have any non-financial competing interests in relation to this
paper?
If you can answer no to all of the above, write 'I declare that I have no competing
interests' below. If your reply is yes to any, please give details below.
I declare that I have no competing interests.
I agree to the open peer review policy of the journal. I understand that my name
will be included on my report to the authors and, if the manuscript is accepted
for publication, my named report including any attachments I upload will be
posted on the website along with the authors' responses. I agree for my report to
be made available under an Open Access Creative Commons CC-BY license
(
http://creativecommons.org/licenses/by/4.0/
). I understand that any comments
which I do not wish to be included in my named report can be included as
confidential comments to the editors, which will not be published.
I agree to the open peer review policy of the journal
Reviewer’s report
Title: 
Deep Learning for Sensitive Detection of Helicobacter Pylori in Gastric
Biopsies
Version: 
0 
Date: 
20 Jan 2020
Reviewer: 
Yu-Ching Tsai
Reviewer's report:
In the present study, the authors designed a deep-learning-based decision support
algorithm that could be applied to more sensitively detect the presence of H.
pylori on slides of gastric biopsies. It is interesting and could decrease the burden
of the pathologist. There are some comments about this manuscript:
1. The artificial intelligence algorithm designed by the authors had high
sensitivity and negative predictive value. Thus it would be a suitable tool for
screening and narrowing the scope of H. pylori detection. However, because its
specificity was 
lower than the microscopic studies by the pathologist. Thus it
might still need manual evaluations by the pathologists after positive H. pylori
infection "picked up" by the computer algorithm to achieve good accuracy for H.
pylori detection. 
2. The Figure 5 is not found in the PDF file of the manuscript.
3. The authors are suggested to clarify the meanings of the proportions in tables
in Fig. 3 and Fig. 4. What the numbers and the proportions stand for? For
example, in table of Fig. 3, in the column of Giemsa stain, 35/347=10% but
172/347= 50%, not 37%. Thus the data in the tables shall be reviewed and
corrected if any error.
Are the methods appropriate and well described?
If not, please specify what is required in your comments to the authors.
Yes
Does the work include the necessary controls?
If not, please specify which controls are required in your comments to the
authors.
Yes
Are the conclusions drawn adequately supported by the data shown?
If not, please explain in your comments to the authors.
Unable to assess
Are you able to assess any statistics in the manuscript or would you
recommend an additional statistical review?
If an additional statistical review is recommended, please specify what aspects
require further assessment in your comments to the editors.
I recommend additional statistical review
Quality of written English
Please indicate the quality of language in the manuscript:
Acceptable
Declaration of competing interests
Please complete a declaration of competing interests, considering the following
questions:
1
. 
Have you in the past five years received reimbursements, fees, funding, or
salary from an organisation that may in any way gain or lose financially
from the publication of this manuscript, either now or in the future?
2
. 
Do you hold any stocks or shares in an organisation that may in any way
gain or lose financially from the publication of this manuscript, either now
or in the future?
3
. 
Do you hold or are you currently applying for any patents relating to the
content of the manuscript?
4
. 
Have you received reimbursements, fees, funding, or salary from an
organization that holds or has applied for patents relating to the content of
the manuscript?
5
. 
Do you have any other financial competing interests?
6
. 
Do you have any non-financial competing interests in relation to this
paper?
If you can answer no to all of the above, write 'I declare that I have no competing
interests' below. If your reply is yes to any, please give details below.
I declare that I have no competing interests.
I agree to the open peer review policy of the journal. I understand that my name
will be included on my report to the authors and, if the manuscript is accepted
for publication, my named report including any attachments I upload will be
posted on the website along with the authors' responses. I agree for my report to
be made available under an Open Access Creative Commons CC-BY license
(
http://creativecommons.org/licenses/by/4.0/
). I understand that any comments
which I do not wish to be included in my named report can be included as
confidential comments to the editors, which will not be published.
I agree to the open peer review policy of the journal
Please review this paper.
user
Reviewer’s report
Title: 
Deep Learning for Sensitive Detection of Helicobacter Pylori in Gastric
Biopsies
Version: 
0 
Date: 
20 Jan 2020
Reviewer: 
Yu-Ching Tsai
Reviewer's report:
In the present study, the authors designed a deep-learning-based decision support
algorithm that could be applied to more sensitively detect the presence of H.
pylori on slides of gastric biopsies. It is interesting and could decrease the burden
of the pathologist. There are some comments about this manuscript:
1. The artificial intelligence algorithm designed by the authors had high
sensitivity and negative predictive value. Thus it would be a suitable tool for
screening and narrowing the scope of H. pylori detection. However, because its
specificity was 
lower than the microscopic studies by the pathologist. Thus it
might still need manual evaluations by the pathologists after positive H. pylori
infection "picked up" by the computer algorithm to achieve good accuracy for H.
pylori detection. 
2. The Figure 5 is not found in the PDF file of the manuscript.
3. The authors are suggested to clarify the meanings of the proportions in tables
in Fig. 3 and Fig. 4. What the numbers and the proportions stand for? For
example, in table of Fig. 3, in the column of Giemsa stain, 35/347=10% but
172/347= 50%, not 37%. Thus the data in the tables shall be reviewed and
corrected if any error.
Are the methods appropriate and well described?
If not, please specify what is required in your comments to the authors.
Yes
Does the work include the necessary controls?
If not, please specify which controls are required in your comments to the
authors.
Yes
Are the conclusions drawn adequately supported by the data shown?
If not, please explain in your comments to the authors.
Unable to assess
Are you able to assess any statistics in the manuscript or would you
recommend an additional statistical review?
If an additional statistical review is recommended, please specify what aspects
require further assessment in your comments to the editors.
I recommend additional statistical review
Quality of written English
Please indicate the quality of language in the manuscript:
Acceptable
Declaration of competing interests
Please complete a declaration of competing interests, considering the following
questions:
1
. 
Have you in the past five years received reimbursements, fees, funding, or
salary from an organisation that may in any way gain or lose financially
from the publication of this manuscript, either now or in the future?
2
. 
Do you hold any stocks or shares in an organisation that may in any way
gain or lose financially from the publication of this manuscript, either now
or in the future?
3
. 
Do you hold or are you currently applying for any patents relating to the
content of the manuscript?
4
. 
Have you received reimbursements, fees, funding, or salary from an
organization that holds or has applied for patents relating to the content of
the manuscript?
5
. 
Do you have any other financial competing interests?
6
. 
Do you have any non-financial competing interests in relation to this
paper?
If you can answer no to all of the above, write 'I declare that I have no competing
interests' below. If your reply is yes to any, please give details below.
I declare that I have no competing interests.
I agree to the open peer review policy of the journal. I understand that my name
will be included on my report to the authors and, if the manuscript is accepted
for publication, my named report including any attachments I upload will be
posted on the website along with the authors' responses. I agree for my report to
be made available under an Open Access Creative Commons CC-BY license
(
http://creativecommons.org/licenses/by/4.0/
). I understand that any comments
which I do not wish to be included in my named report can be included as
confidential comments to the editors, which will not be published.
I agree to the open peer review policy of the journal
assistant