PONE-D-25-04141Deep Learning for Pediatric Chest X-Ray Diagnosis: Repurposing a Commercial Tool Developed for AdultsPLOS ONE

Dear Dr. Agarwal,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Thank you for submitting this interesting manuscript to our journal. The use of AI is the most talked about thing in healthcare industry right now and we highly appreciate your work in this field. There is also notable gaps in evidence base for its use case in childhood TB. We are very happy to inform you that this paper has been reviewed by relevant experts and I am also very happy to see the time and effort they have invested in this review. I believe that addressing their comments/feedback will significantly improve the scientific validity of the manuscript. We look forward to the revised submission. 

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Shahriar Ahmed, MBBS, MHE, MPhil

Academic Editor

PLOS ONE

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Additional Editor Comments:

Thank you for submitting this interesting manuscript to our journal. The use of AI is the most talked about thing in healthcare industry right now and we highly appreciate your work in this field. There is also notable gaps in evidence base for its use case in childhood TB. We are very happy to inform you that this paper has been reviewed by relevant experts and I am also very happy to see the time and effort they have invested in this review. I believe that addressing their comments/feedback will significantly improve the scientific validity of the manuscript. We look forward to the revised submission.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is important research that aims to advance the performance of AI CXR models among pediatric patients, a patient group that has not received attention in the digital X-ray space despite the accelerated development of this field for adults. Authors assessed how a CAD algorithm that was previously trained on adults performs among children and found that performance was clinically acceptable, though further cutoff tuning could help further improve.

I have a few comments:

Abstract:

• I recommend naming the commercial tool directly in the abstract so that researchers interested in this tool can identify your paper more quickly.

• Would mention out of the 958 how many had abnormal CXR (n=200) for a bit more context on the study population

• “The diagnostic performance of the AI-tool was validated using standard measures of accuracy using recommended and optimized thresholds to dichotomize the continuous AI output (0-100)” – clarify that the recommended thresholds are for adults.

• I am not a proponent of reporting on the accuracy of an algorithm. Accuracy can be highly misleading in imbalanced datasets as is the case with only 200 positives. For overall performance, I would report the AUC and then the sensitivity/specificity, specifying that this is against the adult-recommended threshold. For the age-stratified results, I would only report the AUCs in the abstract.

Introduction

• Again, would name the actual CXR device here. You don’t introduce what the tool is until the methods.

Methods

• The initials of the radiologists are still “XX”

• Inconsistent nomenclature for figure (“Fig.” vs “Figures”)

• “The following ten findings are detected by the algorithm: consolidation, atelectasis, nodule, fibrosis, calcification, pleural effusion, pneumothorax, pneumoperitoneum, cardiomegaly and mediastinal widening.” – could you provide the N for each of these that were used when training the model on the adult population. Would allow for a comparison with the pediatric cohort.

• You are missing the diagnostic performance metrics (AUC, sens, spec…) of the CAD in the adult population. I see it in figure 5, but this could be missed and should be in the main text to allow for comparison with what is achieved in pediatrics.

Results

• When discussing threshold-based metrics (accuracy, sens, spec, PPV, NPV) it is good to remind the reader that this is benchmarked against the adult threshold.

• The corrected analysis is interesting to highlight the imperfect reference standard (i.e., human radiologist). I think it highlights a limitation of the study that is not acknowledged in the discussion, that there was only one board-certified radiologist reviewing the X-rays compared to the 5 used in the adult study. This is evidenced by the fact that 6/7 corrected scores were actually when the AI was right but the labeller was wrong. I am not sure if re-calculating the diagnostic metrics for the corrected analysis makes sense as this is altering the definition of what the reference actually is. It may be sufficient to descriptively highlight these discrepant cases, and mention this lack of replication of radiological analysis as a limitation in the discussion.

• “we calculated optimized cut-offs for each pathology and their performance metrics” – name the two pathology groups because I initially thought it was for each individual pathology (which is underpowered). It would also be good to have an overall re-calculated thresholds for any abnormal vs normal for pediatric, as was done for adults.

Discussion

• As previously mentioned, acknowledgement of a lack of replication of radiological interpretation in the reference needs to be acknolweged

• Further limitations are that performance cannot be extrapolated to other respiratory conditions (e.g., tuberculosis CAD X-ray interpretation in adults vs in pediatrics cannot draw conclusions from this study). Could go along with the generalizability statement already made.

Tables

• Table 1: the pleuroparenchymal and mediastinal denominator should be 200. Would also be good to show the number who have both as a separate line .

• Inconsistent N for the relevant pathology, pleuro, mediastinal across table 1, 2a and 2b. I expect the number in 1 to match 2a since 2b is the correct. However relevant overall in 1 is N=200 while in 2a is N=198, etc.

• Figure 1: there is a lot of text, would be good to cut down. No definition of what (a) and (b) represent. Would be good to also show performance metrics for both adults and pediatrics so that it gives all the info in one place.

• Figure 2: doesn’t add much to the manuscript, can be deleted.

Reviewer #2: Dear Editor, authors,

I am grateful for the opportunity to review the manuscript “Deep Learning for Pediatric Chest X-Ray Diagnosis: Repurposing a Commercial Tool Developed for Adults”. The authors note that there are few dedicated AI tools for evaluating pediatric chest radiographs. They, therefore, set out to study how a tool developed for the analysis of chest radiographs of adults performs when used (“repurposed”) for pediatric chest radiographs.

Title:

Fine.

Keywords:

Please revisit the keywords. My suggestion: Pediatric; Chest Radiograph; Artificial Intelligence

Abstract:

Overall informative. Please consider the following suggestions:

Rare --> largely unavailable; Any discordant findings --> All discordant findings

The authors state: “The diagnostic performance of the AI-tool was validated using standard measures of accuracy using recommended and optimized thresholds to dichotomize the continuous AI output (0-100).” I think some readers might struggle with this and therefore ask the authors to elaborate on this.

The performance was high for relevant findings. Please elaborate what relevant means in this context.

The readers may find the categorization according to the 7-year cut-off somewhat arbitrary. The authors may want to touch on this point, too.

Competing Interests:

The authors have declared that no competing interests exist. With all due respect, this statement must be further justified given that the developer of the studied software tool supported the study and that one of the authors is affiliated with the company itself. I also notice that the authors report on page 17 that the developer financed two of the key authors through their institution. I warmly welcome full transparency. If possible, consider stating that although the developer funded the study and one of the authors is employed by the company, all authors had access to the data and the decision to publish was based on the findings, not by the request of the company.

Ethics Statement:

Please also explicitly state the need for written informed consent was waived by the ethics committee and the study adheres to the national laws and regulations, if applicable.

Abbreviations:

I note the authors use the terms chest radiograph and chest X-ray. Please stick to chest radiograph through and through.

Introduction:

Although the introduction is well-written and reasoned, I think a more thorough review of existing tools for pediatric chest radiographs is very much needed. I therefore suggest well-conducted literature search and addition of a table summarizing the key results.

Materials and Methods:

Patient population:

The authors should describe the study as a single-center retrospective cohort study.

Perhaps the authors would like to explain why they chose to include 1000 consecutive chest radiographs. What led them to think this is an appropriate size for the cohort, not for example 900 or 1100 chest radiographs?

I think it’s reasonable to omit the exclusion criteria 2 because the authors already stated they included radiographs of older children. Rather, please reason why the study was limited to children aged between 2-14 years.

Ethics statement: please make sure it’s concordant with the ethics statement as discussed earlier.

Reference standard reading:

Please consider the following suggestions:

Fig. 3 --> Figure 3.

Please explain why all the available information, including the lab results, CT scans, and clinical history were taken into consideration when laying out the reference reads. After all, this information was not available to the software. While I of course support the use of all available information when reading the radiographs, one might argue that a truer performance assessment could have been achieved without considering other information.

Chest radiographs:

Were the chest radiographs caputed in supine, standing or sitting position?

AI-Algorithm:

Rather than speaking of gender, please speak of sex. Men --> males, women --> females.

Was Lunit involved in the decision to publish the results?

Could you please also touch on the training set used in the validation study. I think it would be very important to discuss the homogeneity/heterogeneity of the dataset particularly in relation to sex, age and ethnicity of the patients.

Statistical Analysis:

I wish to applaud for the well-written chapter. Normally this section is overlooked. I find it easy to immerse into the Results section after reading this chapter. However, I ask the authors to elaborate on the age categorization.

Results:

All in all, this section is well written and nicely backed by the tables. However, as the tables should stand alone, I think the authors could think of further strengthening the tables by informing the reader where the numbers come from. For example, it should be clear what is being used as the gold standard and why the number of relevant overall pathologies differ between the tables.

Please also report the results for girls/boys separately in Table 1.

I’d also encourage the authors to report the ethnicities of the patients.

Discussion:

As I suggested earlier, I prompt a more thorough review and discussion of the existing tools for pediatric chest radiograph assessment.

I’d like the authors to also discuss whether the results would translate to all demographic subgroups of children. Particularly, does ethnicity have an effect?

Table 2b:

“a simplified manner to avoid bias due to low number of cases.” � please rewrite for clarity.

Figure 1:

Please also indicate the study setting: this is a single-center retrospective cohort study.

It said in the results 200 (20.9%) of the chest radiographs had at least one pathology. Figure 1 seems to suggest there were 198 chest radiographs with abnormal findings.

Of note, it seems the fonts are widely inconsistent between the Figures.

Figure 2:

Please name the Figure descriptively keeping in mind that the Figure and the description should be able to “stand alone”, i.e., be clear without reading the text.

If the name of the title refers to the CONSORT reporting system, the word should be fully capitalized.

Please correct the inconsistent spacings.

Figure 3:

There seems to be some kind of an annotation in the upper right corner of the A part. Was this intentional?

Figure 4:

OK, very informative.

Figure 5:

Not sure if the figure is needed. I would not oppose its inclusion, however, I feel it does not really add too much information. Should the authors want to keep it, I’d ask them to avoid abbreviations sens, PPV, NOV and spec.

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Reviewer #1: No

Reviewer #2: No

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Deep Learning for Pediatric Chest X-Ray Diagnosis: Repurposing a Commercial Tool Developed for Adults

PONE-D-25-04141R1

Dear Dr. Agarwal,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Shahriar Ahmed, MBBS, MHE, MPhil

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

We would like to congratulate the authors for successfully addressing all comments raised by the reviewers. Thank you for considering this journal for publishing your important work. We hope that you will consider us again for your future publications.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: Thank you for addressing the comments. I am happy with the revisions and support the publication of this interesting manuscript.

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Reviewer #1: No

Reviewer #2: No

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