Title: Establishing an AI Model and Application for Automated Capsule Endoscopy
Recognition Based on Convolutional Neural Networks (with Video)
Version: 2 Date: 08 Oct 2024
Reviewer:  Panagiotis Tsibouris
Reviewer's report:
During capsule endoscopy reading a flowing video rather than single pictures were evaluated
and specific gravity of every picture is examined within the context of neighburing ones Authors
should stress this difference even from the abstract Methods Quality of bowel preparation should
be taken into consideration Authors have selected rather homogenious populations for every
capsule brand ; 3 different hospitals each one of them using a different capsule Why were
neoplasias and submucosal lesions excluded? Was there any access to verification of lesions after
enteroscopy or MR enterography avaible? Otherwise the goldstandard should be stressed Why
the pictures selected were representative to everyday practice should be explained Statistical
method to access the parameters measured should be presented Results Significant results should
be presented in the text and not only in tables Pictures from different capsule endoscopy types
should be presented since the vast majority of pictures come from PillCam
Title: Establishing an AI Model and Application for Automated Capsule Endoscopy
Recognition Based on Convolutional Neural Networks (with Video)
Version: 2 Date: 08 Oct 2024
Reviewer:  Panagiotis Tsibouris
Reviewer's report:
During capsule endoscopy reading a flowing video rather than single pictures were evaluated
and specific gravity of every picture is examined within the context of neighburing ones Authors
should stress this difference even from the abstract Methods Quality of bowel preparation should
be taken into consideration Authors have selected rather homogenious populations for every
capsule brand ; 3 different hospitals each one of them using a different capsule Why were
neoplasias and submucosal lesions excluded? Was there any access to verification of lesions after
enteroscopy or MR enterography avaible? Otherwise the goldstandard should be stressed Why
the pictures selected were representative to everyday practice should be explained Statistical
method to access the parameters measured should be presented Results Significant results should
be presented in the text and not only in tables Pictures from different capsule endoscopy types
should be presented since the vast majority of pictures come from PillCam
Please review this paper.
user
Title: Establishing an AI Model and Application for Automated Capsule Endoscopy
Recognition Based on Convolutional Neural Networks (with Video)
Version: 2 Date: 08 Oct 2024
Reviewer:  Panagiotis Tsibouris
Reviewer's report:
During capsule endoscopy reading a flowing video rather than single pictures were evaluated
and specific gravity of every picture is examined within the context of neighburing ones Authors
should stress this difference even from the abstract Methods Quality of bowel preparation should
be taken into consideration Authors have selected rather homogenious populations for every
capsule brand ; 3 different hospitals each one of them using a different capsule Why were
neoplasias and submucosal lesions excluded? Was there any access to verification of lesions after
enteroscopy or MR enterography avaible? Otherwise the goldstandard should be stressed Why
the pictures selected were representative to everyday practice should be explained Statistical
method to access the parameters measured should be presented Results Significant results should
be presented in the text and not only in tables Pictures from different capsule endoscopy types
should be presented since the vast majority of pictures come from PillCam
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