Title: A newly developed deep learning-based system for automatic detection and classification
of small bowel lesions during double-balloon enteroscopy examination
Version: 5 Date: 09 Oct 2023
Reviewer's report:
Dear authors,
Thank you for your clear point-to-point reply.
I do believe this version to be much approved, and only have some minor mainly language issues.
With regards to table 2-4, I still feel the presentation of accuracy makes the data difficult to
comprehend, as compared to sensitivity and specificity, especially because there are only 2
videos with diverticula and 8 with angioectasia. In my opinion it is statistically incorrect to
analyze the accuracy in this manner.
My suggestions:
Use classes instead of classifications throughout the manuscript.
Be clear on what system you are writing about. You write 'this system' or 'the system' multiple
times, why not use 'The CAD system' or 'ENDOANGEL-DBE'? In my opinion that would be
more concise and improve the clarity in the language.
Use a simular conclusion in your abstract as in your article, I would put emphasis on that it is
especially useful for novices.
Page 4 line 19: potential to reduce diagnostic error
Page 7 line 22: enclosed
Page 11 line 7: delete Who were unaware of the standard answer process
Page 13 line 3: the accuracies of the CAD system and the human observers for the different
classes of lesions is shown in ...
Page 14 line 6-9: delete, is a repetition of your introduction
Page 15 line 2: delete, is a repetition
Page 16 line 1: recent instead of current
Line 2: delete DAE, if you are not going to use the word again, the abbreviation is irrelevant
Line 17: participated instead of were Use
Line 22: were humans
Page 17 line 11: consider writing a bit more about the relevance and possiblities of such a new
AI system
Title: A newly developed deep learning-based system for automatic detection and classification
of small bowel lesions during double-balloon enteroscopy examination
Version: 5 Date: 09 Oct 2023
Reviewer's report:
Dear authors,
Thank you for your clear point-to-point reply.
I do believe this version to be much approved, and only have some minor mainly language issues.
With regards to table 2-4, I still feel the presentation of accuracy makes the data difficult to
comprehend, as compared to sensitivity and specificity, especially because there are only 2
videos with diverticula and 8 with angioectasia. In my opinion it is statistically incorrect to
analyze the accuracy in this manner.
My suggestions:
Use classes instead of classifications throughout the manuscript.
Be clear on what system you are writing about. You write 'this system' or 'the system' multiple
times, why not use 'The CAD system' or 'ENDOANGEL-DBE'? In my opinion that would be
more concise and improve the clarity in the language.
Use a simular conclusion in your abstract as in your article, I would put emphasis on that it is
especially useful for novices.
Page 4 line 19: potential to reduce diagnostic error
Page 7 line 22: enclosed
Page 11 line 7: delete Who were unaware of the standard answer process
Page 13 line 3: the accuracies of the CAD system and the human observers for the different
classes of lesions is shown in ...
Page 14 line 6-9: delete, is a repetition of your introduction
Page 15 line 2: delete, is a repetition
Page 16 line 1: recent instead of current
Line 2: delete DAE, if you are not going to use the word again, the abbreviation is irrelevant
Line 17: participated instead of were Use
Line 22: were humans
Page 17 line 11: consider writing a bit more about the relevance and possiblities of such a new
AI system
Please review this paper.
user
Title: A newly developed deep learning-based system for automatic detection and classification
of small bowel lesions during double-balloon enteroscopy examination
Version: 5 Date: 09 Oct 2023
Reviewer's report:
Dear authors,
Thank you for your clear point-to-point reply.
I do believe this version to be much approved, and only have some minor mainly language issues.
With regards to table 2-4, I still feel the presentation of accuracy makes the data difficult to
comprehend, as compared to sensitivity and specificity, especially because there are only 2
videos with diverticula and 8 with angioectasia. In my opinion it is statistically incorrect to
analyze the accuracy in this manner.
My suggestions:
Use classes instead of classifications throughout the manuscript.
Be clear on what system you are writing about. You write 'this system' or 'the system' multiple
times, why not use 'The CAD system' or 'ENDOANGEL-DBE'? In my opinion that would be
more concise and improve the clarity in the language.
Use a simular conclusion in your abstract as in your article, I would put emphasis on that it is
especially useful for novices.
Page 4 line 19: potential to reduce diagnostic error
Page 7 line 22: enclosed
Page 11 line 7: delete Who were unaware of the standard answer process
Page 13 line 3: the accuracies of the CAD system and the human observers for the different
classes of lesions is shown in ...
Page 14 line 6-9: delete, is a repetition of your introduction
Page 15 line 2: delete, is a repetition
Page 16 line 1: recent instead of current
Line 2: delete DAE, if you are not going to use the word again, the abbreviation is irrelevant
Line 17: participated instead of were Use
Line 22: were humans
Page 17 line 11: consider writing a bit more about the relevance and possiblities of such a new
AI system
assistant