AI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study

Li Sun; Yangyang Fan; Shan Shi; Minghong Sun; Yunyao Ma; Kuo Zhang; Feng Zhang; Huan Liu; Tong Yu; Haibin Tong; Xuedong Yang

doi:10.1371/journal.pone.0316732

Peer Review History

Original SubmissionSeptember 10, 2024
22 Nov 2024 Decision Letter - Priti Chaudhary, Editor PONE-D-24-35102AI-Assisted Radiologists vs. Standard Double Reading for Rib Fracture Detection on CT Images: A Real-World Clinical StudyPLOS ONE Dear Dr. Yang, 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 Jan 06 2025 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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Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 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: The manuscript is interesting and addressesed valuable findings. Introduction was good written and includes the aim of the study. Materials and methods were good designed. Results were good illustrated . Discussion was good written. Reviewer #2: The authors present an interesting study regarding double reading vs. AI alone vs. AI assisted interpretations of rib fractures on CT scan. The authors demonstrate that AI assisted reading of CT scans for rib fractures may help improve work flow and and potentially higher sensitivity/specificity of diagnosing rib fractures. Further background information on the radiologists reading the scans will help prove the utility of AI. Stratifying the radiologists by their own years of practice vs. AI/AI-assisted scans would be of great interest and help improve the impact of this study. What about pathologies associated with rib fractures? Such as PTX, hemothorax, pulmonary contusion, cardiac contusion, intercostal neurovascular bundle injury would all be of great clinical importance in addition to identifying rib fractures. If the authors could include the specifications of the CT scanner used and the image viewing software as well would be helpful. Furthermore, the specifications of the computer system and monitors utilized by the radiologist would help elucidate these findings a bit more. Instead of just double read CT scans, the authors should also include data from single read CT scans to see if there is an even greater need of AI assistance for radiologists operating in rural areas. Adding the time required to perform double read vs. Ai/AI assisted reads would be very helpful to help identify any improvements in workflow and cost analysis. ****** 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: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. 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Revision 1
13 Dec 2024 Author Response Dear Editors and Reviewers, Thank you for your thorough review and insightful comments on our manuscript. We have carefully considered each point and have provided our responses below: Response to Reviewer #1: Thank you for your positive comments on our manuscript. We are gratified to hear that you found the study interesting and well-executed. Your comments encourage us and affirm the value of our research. Response to Reviewer #2: Thank you for your insightful comments. We have addressed each of your points as follows: o Background Information on Radiologists: We appreciate your suggestion to include more background information on the radiologists involved in the study. We have expanded the relevant section in the main manuscript (Materials and Methods; 4. Image interpretation, lines 5-9) to include their years of practice, which we believe better demonstrates the utility of AI. The updated sentence now reads: " AII primary reports were conducted by 2 residents (2nd and 3rd year of residency; female, 1) and 5 attending radiologists (4 to 8 years of experience; mean, 6.6±1.7 years of experience; female, 5), with secondary readings performed by senior radiologists with over 10 years of experience (See S1 Table for details)." Additionally, we have included more comprehensive background details in the supplementary material (please see S1 Table). Additional details are provided in the supplementary material (S1 Table). While we agree that stratification by years of practice is valuable, the size constraints of our study currently limit the feasibility of conducting such an analysis. We hope to explore this issue with greater rigor in future endeavors, specifically through larger-scale, multicenter studies. o Pathologies Associated with Rib Fractures: We have heeded your advice and expanded our analysis to include associated pathologies. Data on the incidence of pneumothorax, pulmonary contusions, and hemothorax have been added to the "Materials and Methods 1. Dataset collection and access" section. The updated sentence reads: "Intrathoracic injuries associated with RFs included pneumothorax (5.3%, 10 of 188), pulmonary contusions (2.1%, 4 of 188), and hemothorax (1.1%, 2 of 188)." This addition is intended to provide a more comprehensive clinical context, aligning with the importance you have highlighted. We would also like to express our gratitude for your insightful proposal. The inclusion of other pathologic changes associated with rib fractures (RFs) more fully reflects the clinical conditions of the patients in our study. As the data indicate, the incidence of pathologies associated with RFs in this study is roughly the same as the results of some of the previous studies [1], but slightly lower than others [2]. While, the distribution of fracture characteristics, as shown in S2 Fig, is generally consistent with previous researches [3,4]. o CT Scanner and Image Viewing Software and Monitor Specifications: We have included the specifications of the image viewing software and computer system used by the radiologists in the "Materials and Methods 1. Dataset collection and access" section. The updated sentence reads: “All images were reviewed through the picture archiving and communication system (PACS) (3.0.30500.213; Hinacom Software and Technology, Beijing, China) on a Windows 7 system (Microsoft Corporation, Redmond, Washington, United States) using professional radiographic monitors (JUSHA-M52C, JUSHA-M53AA, JUSHA-M33B and JUSHA-M33D; Nanjing Jusha Display Technology, Nanjing, China).” Additionally, the specifications of the CT scanners are detailed: “Scans were performed on two Siemens CT scanners (Emotion 16 and SOMATOM Definition Flash) with a 120 kV tube voltage and automated dose modulation….” o Data from Single Read CT Scans: Regrettably, the collection of single read data was not feasible within the scope of our current study design. Our aim was to assess the performance of radiologists using AI assistance in a real-world clinical environment, which led us to integrate target patient images into the daily workflow through the PACS system. Given the full integration of AI-assisted diagnostic technology into our department's rib fracture diagnosis protocol, conducting single radiologist reads without AI assistance would not be practical without the radiologists' awareness, as it would deviate from our standard clinical practice. We acknowledge the importance of comparing single radiologist diagnosis with AI-assisted diagnosis, and we are aware of existing literature on this topic. Studies have consistently demonstrated that the incorporation of AI assistance in radiological diagnosis leads to a substantial enhancement in the sensitivity of single radiologists for rib fractures, concurrently preserving diagnostic specificity. The reported increase in sensitivity varies depending on the study design, ranging from 9.5% to 23.9% [5-7]. This underscores the potential clinical value of AI in enhancing diagnostic accuracy, especially in settings where radiologist resources may be limited, such as rural areas. We appreciate your guidance and will consider incorporating single read data in future prospective studies to further explore the impact of AI in various clinical scenarios, including those you've highlighted. o Time Required for Double Read vs. AI-Assisted Reads: We agree that evaluating diagnostic time is a valuable research direction. Prior studies have shown that AI assistance significantly reduces diagnostic time for radiologists, with savings ranging from 73.9 seconds to 771 seconds depending on study conditions [5,7]. As a retrospective study, we were unable to directly measure diagnostic time. This limitation is acknowledged in the "Discussion" section, specifically in the penultimate paragraph where we discuss the limitations of our study. The updated content is as follows: " Third, since this is a retrospective study, the time spent on diagnosis cannot be calculated. Previous studies have consistently demonstrated the ability of AI to shorten the reading time of radiologist in diagnosing RFs in chest CT. With the aid of AI, report time savings ranged from approximately 73.9 seconds to 771 seconds, depending on the study conditions..." Reference: [1] Tignanelli CJ, Rix A, Napolitano LM, Hemmila MR, Ma S, Kummerfeld E. Association Between Adherence to Evidence-Based Practices for Treatment of Patients With Traumatic Rib Fractures and Mortality Rates Among US Trauma Centers. JAMA Netw Open. 2020 Mar 2;3(3):e201316. [2] Nummela MT, Bensch FV, Pyhältö TT, Koskinen SK. Incidence and Imaging Findings of Costal Cartilage Fractures in Patients with Blunt Chest Trauma: A Retrospective Review of 1461 Consecutive Whole-Body CT Examinations for Trauma. Radiology. 2018 Feb;286(2):696-704. doi: 10.1148/radiol.2017162429. [3] Fukushima K, Kambe M, Aramaki Y, Ichikawa Y, Isshiki Y, Nakajima J, Sawada Y, Oshima K. Evaluation of injury threshold from the number of rib fracture for predicting pulmonary injuries in blunt chest trauma. Heliyon. 2023 Apr 6;9(4):e15278. [4] Barber I, Perez-Rossello JM, Wilson CR, Kleinman PK. The yield of high-detail radiographic skeletal surveys in suspected infant abuse. Pediatr Radiol 2015;45(1):69-80. [5] Zhou QQ, Wang J, Tang W, Hu ZC, Xia ZY, Li XS, et al. Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility. Korean J Radiol. 2020;21(7):869-879. [6] Wu M, Chai Z, Qian G, Lin H, Wang Q, Wang L, et al. Development and Evaluation of a Deep Learning Algorithm for Rib Segmentation and Fracture Detection from Multicenter Chest CT Images. Radiol Artif Intell. 2021;3(5):e200248. [7] Jin L, Yang J, Kuang K, Ni B, Gao Y, Sun Y, et al. Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet. EBioMedicine. 2020;62:103106. We hope that these revisions adequately address your concern and strengthens the relevance of our study to clinical practice. We are committed to ensuring that our work is thorough and clinically relevant. Thank you again for your constructive feedback. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0316732.r002
17 Dec 2024 Decision Letter - Priti Chaudhary, Editor AI-Assisted Radiologists vs. Standard Double Reading for Rib Fracture Detection on CT Images: A Real-World Clinical Study PONE-D-24-35102R1 Dear Dr. Xuedong Yang, 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, Priti Chaudhary, M.S. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0316732.r003
Formally Accepted
14 Jan 2025 Acceptance Letter - Priti Chaudhary, Editor PONE-D-24-35102R1 PLOS ONE Dear Dr. Yang, 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 Dr. Priti Chaudhary Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0316732.r004

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