Machine learning-based pipeline for automated intracerebral hemorrhage and drain detection, quantification, and classification in non-enhanced CT images (NeuroDrAIn)

Samer Elsheikh; Ahmed Elbaz; Alexander Rau; Theo Demerath; Elias Kellner; Ralf Watzlawick; Urs Würtemberger; Horst Urbach; Marco Reisert

doi:10.1371/journal.pone.0316003

Peer Review History

Original SubmissionJuly 18, 2024
7 Nov 2024 Decision Letter - Ziyu Qi, Editor PONE-D-24-26212Accuracy of intracerebral hemorrhage drain detection, quantification and classification of coverage in computed tomography using machine learningPLOS ONE Dear Dr. Elsheikh, 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. Please submit your revised manuscript by Dec 22 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Horst Urbach: Received honoraria for lectures from Biogen, Eisai, Mbits and Lilly, is supported by German Federal Ministry of Education and Research, and is coeditor of Clin Neuroradiol. Elias Kellner: Shareholder of and received fees from VEObrain GmbH, Freiburg, Germany. 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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: The manuscript presents a promising approach to the automated detection of intracerebral hemorrhage using convolutional neural networks. It effectively addresses a significant clinical need by proposing a novel detection pipeline and demonstrating its potential efficacy. However, the study is limited by the small dataset size, which may affect the robustness and generalizability of the findings. While the results are promising, the manuscript would benefit from a more detailed discussion of the dataset limitations and their impact on the results. Additionally, enhancements to the title and abstract are needed to better reflect the study's focus and key findings. Overall, the research provides valuable insights but requires further validation and refinement. Please see below for my detailed comments. Title: • The title should clearly indicate the focus of the study, specifically emphasizing the automated nature of the pipeline and its application to ICH detection. • Aim for a concise title that effectively conveys the essence of the study without being overly complex. Abstract: • Clearly state the main aim of the study, such as developing and validating an automated pipeline for ICH detection from imaging data. Provide context on the importance of the research and what gaps it addresses. • Discuss the implications of the findings and their significance for clinical practice or future research. Highlight the contribution of the study to the field. Introduction: • Context and Background: The introduction provides a good overview of intracerebral hemorrhage and its clinical significance. However, it could benefit from a more detailed discussion of current detection methods and their limitations to set up the research gap more clearly. • Research Gap and Objectives: The manuscript should explicitly state the research gap it addresses and the specific objectives of the study. This will help in aligning the reader’s understanding with the study’s contributions. Methods: • Data Description: The manuscript lacks detail on the dataset size and composition. Given the small data size, a more thorough description of the dataset, including the number of images, sources, and any potential biases, is crucial. • Model Architecture: More detail is needed on the convolutional neural network architecture used, including the number of layers, types of layers, and any hyperparameters that were tuned. • Validation Strategy: The method of model validation should be described in greater detail. Explain if cross-validation, a holdout set, or other techniques were used, and how they contribute to the robustness of the results. • Offer a clearer explanation of the convolutional neural network architecture used. Include details on the layers, activation functions, and any hyperparameters. • Elaborate on the validation process, including any cross-validation techniques used. Specify how performance metrics were calculated and provide justification for the chosen methods. • The limited size of the dataset is a critical concern. Discuss how the small data size might impact the training and generalization of the model. Results: • Reflect on how the small dataset size might influence the performance metrics and results. Discuss any potential overfitting issues and how they were addressed. Provide insights into how the model's performance might vary with a larger dataset. Discussion: • Interpretation of Results: The discussion should offer a deeper interpretation of the results, including how the performance metrics compare with those from previous studies. • Limitations: There should be a more explicit discussion of the study’s limitations, especially concerning the small dataset size. Address how these limitations might affect the generalizability and reliability of the results. • Future Work: Suggestions for future research should be more detailed, including how to address the limitations and potential improvements in dataset size or model complexity. Conclusion: • Summary: Summarize the main findings of the study and their significance. Emphasize the contribution of the research to the field of ICH detection and any practical implications for clinical practice. • Implications: Clearly outline the potential impact of the study on current practices and future research directions. Reviewer #2: This study applies machine learning techniques to the detection of drain in CT scan images of intracranial hemorrhages after minimally invasive intervention, as well as to the quantification and classification of drainage coverage. Article well structured and written. Methodology is correct. The number of patients included in the training and validation subgroup is exceedingly small (21 and 3, respectively). A similar situation is observed with the testing group, which initially comprised only 5 patients but was subsequently expanded to 44 in a second phase. This expansion allows for a more reliable evaluation of the algorithm's validity in clinical practice. It may seem surprising that, with such a limited number of subjects in the training set, acceptable accuracy values are achieved for detection (97%), coverage (86%), and correct catheter positioning (88%). I believe this is an example where the task is relatively straightforward for an algorithm, and the errors exhibited could be minimized by increasing the number of training cases. The authors acknowledge the significant limitations of their study in the discussion section. Despite these limitations, I consider this to be a well-designed study with useful results, particularly in the context of a relatively uncommon pathology for which it may be challenging to obtain a larger patient cohort. Minor Comments - I recommend avoiding the use of abbreviations in the abstract. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: Yes: Juan-Jose Beunza ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0316003.r001
Revision 1
26 Nov 2024 Author Response The responses to the editorial offfice concerning formatting and revisions of the ethics statement and data availabliity are included point for point in the new covering letter. Please find below the point for point responses to the reviewers. Point for Point Response Reviewer 1: The manuscript presents a promising approach to the automated detection of intracerebral hemorrhage using convolutional neural networks. It effectively addresses a significant clinical need by proposing a novel detection pipeline and demonstrating its potential efficacy. However, the study is limited by the small dataset size, which may affect the robustness and generalizability of the findings. While the results are promising, the manuscript would benefit from a more detailed discussion of the dataset limitations and their impact on the results. Additionally, enhancements to the title and abstract are needed to better reflect the study’s focus and key findings. Overall, the research provides valuable insights but requires further validation and refinement. Please see below for my detailed comments. Response We thank you for your valuable comments and input. The comments address important points, some were omitted and some were reported too concisely. We made changes to the manuscript, which we believe to have addressed all the points in the comments. Below are point for point responses, highlighting the changes we made, below each comment. Title: • The title should clearly indicate the focus of the study, specifically emphasizing the automated nature of the pipeline and its application to ICH detection. • Aim for a concise title that effectively conveys the essence of the study without being overly complex. Response Original title: Accuracy of intracerebral hemorrhage drain detection, quantification and classification of coverage in computed tomography using machine learning We changed the Title to: “Machine Learning-Based Pipeline for Automated Intracerebral Hemorrhage and Drain Detection, Quantification, and Classification in non-enhanced CT Images (NeuroDrAIn)”. Abstract: • Clearly state the main aim of the study, such as developing and validating an automated pipeline for ICH detection from imaging data. Provide context on the importance of the research and what gaps it addresses. • Discuss the implications of the findings and their significance for clinical practice or future research. Highlight the contribution of the study to the field. Response Original: Background and purpose To develop and validate an automated pipeline for intracerebral hemorrhage (ICH) drain detection, quantification of ICH coverage and detection of malpositioned drains. Changed to: Background and purpose External drainage represents a well-established treatment option for acute intracerebral hemorrhage. The current standard of practice includes post-operative CT imaging, which is subjectively evaluated. The implementation of an objective, automated evaluation of postoperative studies would enhance diagnostic accuracy and facilitate the scaling of research projects. The objective is to develop and validate a fully automated pipeline for intracerebral hemorrhage and drain detection, quantification of intracerebral hemorrhage coverage, and detection of malpositioned drains. Introduction: • Context and Background: The introduction provides a good overview of intracerebral hemorrhage and its clinical significance. However, it could benefit from a more detailed discussion of current detection methods and their limitations to set up the research gap more clearly. Response We added the following sentences: Moreover, the lack of research on standardized or clinically validated criteria for detecting malpositioned drains represents a significant limitation to both clinical and scientific evaluation. Consequently, the assessment of these images remains highly subjective. • Research Gap and Objectives: The manuscript should explicitly state the research gap it addresses and the specific objectives of the study. This will help in aligning the reader’s understanding with the study’s contributions. Response We added the following: The application of automated segmentation techniques using convolutional neural networks to medical images provides the potential for the generation of quantitative data. If validated, this data could facilitate objective clinical assessment and serve as a foundation for further scientific research. Methods: • Data Description: The manuscript lacks detail on the dataset size and composition. Given the small data size, a more thorough description of the dataset, including the number of images, sources, and any potential biases, is crucial. Response To improve the presentation of information, we added the number of images belonging to each group to the methods sections. We explicitly pointed out the steps we have taken to reduce bias. • Model Architecture: More detail is needed on the convolutional neural network architecture used, including the number of layers, types of layers, and any hyperparameters that were tuned. • Validation Strategy: The method of model validation should be described in greater detail. Explain if cross-validation, a holdout set, or other techniques were used, and how they contribute to the robustness of the results. • Offer a clearer explanation of the convolutional neural network architecture used. Include details on the layers, activation functions, and any hyperparameters. Response Instead of referring the reader to the previously published work we added a 1-paragraph summary describing the model architecture, the development and tuning process, the validation strategy and the rationale behind it and the evaluation metrics. We also highlighted the most relevant ones. • Elaborate on the validation process, including any cross-validation techniques used. Specify how performance metrics were calculated and provide justification for the chosen methods. • The limited size of the dataset is a critical concern. Discuss how the small data size might impact the training and generalization of the model. Response This was a concern while performing the study. We deemed it important to maintain the same data partitioning as that used in developing the CNN to avoid data leakage, so we decided against a repartitioning of the images. We added this paragraph: The small size of our data set may present challenges in developing a predictive classification model. To prevent overfitting we employed regularized models, but did not exclude any of the more complex model families. To limit bias we utilized leave-one-out cross validation, which would maximize the amount of data during training. Finally, we tested model robustness on an independent testing dataset. As a thorough evaluation of the performance could prove difficult, we included the 95%-confidence intervals in all our results. Still we recognize that a thorough evaluation of model performance will be difficult. Results: • Reflect on how the small dataset size might influence the performance metrics and results. Discuss any potential overfitting issues and how they were addressed. Provide insights into how the model’s performance might vary with a larger dataset. Response We added this sentence: Given the limited size of the dataset, the results obtained from the combined training and validation datasets are not sufficiently robust to thoroughly assess the model performance. However, they can be utilized to compare between different models. We further elaborated on this issue in the discussions section. Discussion: • Interpretation of Results: The discussion should offer a deeper interpretation of the results, including how the performance metrics compare with those from previous studies. Response We repeated the literature search, and still found no methodologically comparable studies, so we expanded our search for studies, that may have tackled a similar problem in other disease states or body regions. We added this paragraph: The application of machine learning for the detection and segmentation of ICH has been extensively studied, with a detection accuracy of 98% and a dice similarity coefficient for segmentation of 0.92 being achieved [16, 17]. These results are comparable to those obtained with the model used in our pipeline [7]. A review of the literature revealed no publications on the development of segmentation models for ICH drains. Furthermore, our search revealed a paucity of published literature examining the evaluation of drain position, whether using clinical parameters or machine learning approaches. We conducted an expanded search for publications evaluating the placement of shunts in cases of hydrocephalus. Previous studies have employed machine learning to predict shunt failure, but using different methodologies in comparison to our proposed pipeline [18, 19]. These studies utilized either multiple morphological and clinical parameters or only the progression of ventricular volume on serial scans for detection of shunt failure, but no quantification of the coverage was attempted. A technical note demonstrated the successful application of machine learning for the identification of the make and model of ventricular shunts [20]. Response In response to the above mentioned point concerning reflection on the effect of data size on the results, we changed an existing paragraph to read as follows: The segmentation model results [7] allowed an accurate quantification of the drain coverage, exhibiting a good concordance (ICC = 0.86) with the GT. This facilitated the graphical visualization of the relationship between the drain and the ICH, and served as input for the development of our classification model. In order to address the limitations of the small dataset, we sought to mitigate the issue of overfitting by employing regularized models [21]. Furthermore, a model with a simple architecture is less susceptible to overfitting than a more complex one [21]. We observed this in our results, as the most effective model (penalized logistic regression) was of a relatively simple architecture, which may indicate a certain degree of overfitting with more complex model architectures. Conversely, to enable the model to encompass a sufficient number of data features, we subjected the model to the greatest possible extent of training data by employing leave-one-out cross-validation. We suspect no significant underfitting, as the model reached an accuracy of 0.98 (95% CI: 0.87 to 1) in the training-validation dataset. The model was able to classify the tip position with high accuracy (AUC-ROC = 0.92 (95% CI: 0.85 to 0.99), accuracy = 0.88 [57 of 65; 95% CI: 0.77 to 0.95]) in the testing group. However, in the complete dataset nine drains drains were incorrectly classified. An examination of the incorrectly classified drains revealed an inherent ambiguity in the drain position, as suggested by the poor interrater agreement (Fleiss’ κ = -0.07, p = 0.7) in this subset. Although the predicted coverage profiles of these drains demonstrated a moderate to good agreement with GT coverage profiles (ICC = 0.74), this variance may have contributed to the incorrect classifications. The limited dataset size precludes the exclusion of potential inherent errors in the classification model, as indicated by the somewhat broader 95%-confidence intervals, which may also be a contributing factor. • Limitations: There should be a more explicit discussion of the study’s limitations, especially concerning the small dataset size. Address how these limitations might affect the generalizability and reliability of the results. Response We added the following to the pre-existing paragraph: Our study had several limitations. First, the size of the patient cohort was relatively limited, which may have resulted in missing some clinical scenarios that were not encountered in the dataset. However, the issue we sought to examine exhibits limited variability, and our pipeline was accurate when evaluated on a consecutive three-year patient cohort. Additionally, the limited sample size may introduce inherent errors in the classification model and may not allow for adequate measurement of model performance. The model may be susceptible to memorization of the dataset, leading to overfitting, or it may fail to capture sufficient features, leading to underfitting. Such limitations may have a negative impact when the model is applied to previously unseen data. Although the generalizability was tested on a consecutive three-year patient, the total number of included patients and scans was relatively small (n = 44, 59 scans). • Future Work: Suggestions for future research should be more detailed, including how to address the limitations and potential improvements in dataset size or model complexity. Response we changed an existing paragraph to read as follows: The proposed pipeline has the potential to facilitate numerous research possibilities and clinical applications. Initially, it seems necessary to perform a more comprehensive validation of the prediction model’s generalizability using a larger dataset and a more thorough testing of a wider range of machine learning models. Following a more robust validation, the pipeline could be applied for scaling data collection in larger population studies, with the aim of examining the imaging criteria for a correct drain position. In order to achieve the objective of the surgical procedure, which is to reduce ICH volume and improve patient outcome, it would be beneficial to define the imaging criteria associated with these parameters with greater precision. The modular composition of the pipeline allows for straightforward adaptation to a multitude of different tasks. This includes the incorporation of drains with varying working lengths and the application to other disease states using different segmentation models, such as the drainage of subdural hematomas or hydrocephalus. In the forthcoming era of computer-aided diagnosis, further studies could examine whether the graphical representation of the coverage profile and the automated classification would reduce the time needed for the reporting process. This could be accomplished by eliminating the necessity for multiplanar reconstructions and manual measurements. Furthermore, this approach may assist in reducing discrepancies between readers in instances where a borderline positioning of a drain is encountered. Conclusion: • Summary: Summarize the main findings of the study and their significance. Emphasize the contribution of the research to the field of ICH detection and any practical implications for clinical practice. • Implications: Clearly outline the potential impact of the study on current practices and future research directions. Response We expanded the conclusion in the manuscript and in the abstract to include theese points: Our proposed, multi-stage, automated end-to-end pipeline to detect drains following minimal invasive surgery in acute ICH and to quantify and then classify the drain position in relation to ICH achieved accurate results, comparable to experienced human readers. Following further validation, the proposed methodology may be applied as a computer-aided diagnostic tool or for data collection in large population studies in different disease states. Reviewer #2 This study applies machine learning techniques to the detection of drain in CT scan images of intracranial hemorrhages after minimally invasive intervention, as well as to the quantification and classification of drainage coverage. Article well structured and written. Methodology is correct. The number of patients included in the training and validation subgroup is exceedingly small (21 and 3, respectively). A similar situation is observed with the testing group, which initially comprised only 5 patients but was subsequently expanded to 44 in a second phase. This expansion allows for a more reliable evaluation of the algorithm’s validity in clinical practice. It may seem surprising that, with such a limited number of subjects in the training set, acceptable accuracy values are achieved for detection (97%), coverage (86%), and correct catheter positioning (88%). I believe this is an example where the task is relatively s Attachments Attachment Submitted filename: response_major_revisions_01.docx https://doi.org/10.1371/journal.pone.0316003.r002
5 Dec 2024 Decision Letter - Ziyu Qi, Editor Machine Learning-Based Pipeline for Automated Intracerebral Hemorrhage and Drain Detection, Quantification, and Classification in Non-Enhanced CT Images (NeuroDrAIn) PONE-D-24-26212R1 Dear Dr. Elsheikh, 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. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Ziyu Qi Academic Editor PLOS ONE Additional Editor Comments (optional): The manuscript can be accepted for publication in its present form. Congrats! Reviewers' comments: https://doi.org/10.1371/journal.pone.0316003.r003
Formally Accepted
12 Dec 2024 Acceptance Letter - Ziyu Qi, Editor PONE-D-24-26212R1 PLOS ONE Dear Dr. Elsheikh, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Mr. Ziyu Qi Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0316003.r004

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