Artificial Intelligence-Based Algorithms and Healthcare Applications of Respiratory Inductance Plethysmography: A Systematic Review

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

In this manuscript, the authors provide a very detailed description of respiratory inductance plethysmography (RIP), including an explanation of pulmonary function tests, an illustration of RIP, the procedure for study selection, and the use of RIP in detecting various diseases. The paper is well-written and well-formatted. However, the title of this paper is “Artificial Intelligence-based Algorithms and Healthcare Applications of Respiratory Inductance Plethysmography: A Systematic Review,” yet only one paragraph discusses machine learning without any comprehensive descriptions of its applications with RIP data. This is not acceptable for publication. I suggest rewriting the results section to include a comprehensive discussion of machine learning and artificial intelligence applications with RIP data. There are many studies published on machine learning applications for RIP data. The authors should make a significant effort to ensure these machine learning-related publications are included in this review manuscript.

Author Response

Please see the attachment. 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The review is well organized and easy to read. Methods adopted to perform the literature inspections and criteria to include studies in the review work sound robust.
I appreciated the attention put on practical details and procedures when performing studies in realistic conditions (i.e., not in a laboratory/medical setting). In this sense, wearability and comfortability of devices and related quality of the data collected is an important aspect addressed by the review.

Even if targeting a specific field, that is RIP measurments in medical settings, I think this review will contribute to existing literature.

Author Response

Please see the attachment. 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The paper is relevant to the readers as it serves as a systematic review on the application of Respiratory Inductance Plethysmography (RIP) in healthcare, holding a particular emphasis on its integration with machine learning techniques.

In order to enhance the transparency of your research, I encourage you to further expand on explaining the search strings and Boolean operators used during your the database search, include a breakdown of the types of study designs included and their justification. Consider also adding an explanation of how you conducted the bias assessment process, and also a detailed description of the data extraction process, mentioning the software or tools utilized (if any).

In the results section, you could consider adding more granular details in your synthesis tables. Describe the confidence intervals you used, effect sizes, and provide an analysis of the statistical outcomes for each included study- all these would make the research paper easier to follow and more engaging.

Comments on the Quality of English Language

The quality of English is overall good, but I suggest minor edits for clarity and readability would improve the quality of your manuscript.

Author Response

Please see the attachment. 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors made great effort to rewrite the result and discussion section. Especially the summary on machine learning methods of RIP data. I would like to suggest to accept this manuscript for publication.