{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T21:48:21Z","timestamp":1774475301465,"version":"3.50.1"},"reference-count":56,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/S004033\/1"],"award-info":[{"award-number":["MR\/S004033\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/V012452\/1"],"award-info":[{"award-number":["MR\/V012452\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018693","name":"HORIZON EUROPE Framework Programme","doi-asserted-by":"publisher","award":["101084642"],"award-info":[{"award-number":["101084642"]}],"id":[{"id":"10.13039\/100018693","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["101084642"],"award-info":[{"award-number":["101084642"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>\n                    Chest X-ray (CXR) is a diagnostic tool for cardiothoracic assessment. They make up 50% of all diagnostic imaging tests. With hundreds of images examined every day, radiologists can suffer from fatigue. This fatigue may reduce diagnostic accuracy and slow down report generation. We describe a prototype computer-assisted diagnosis (CAD) pipeline employing computer vision (CV) and Natural Language Processing (NLP). It was trained and evaluated on the publicly available MIMIC-CXR dataset. We perform image quality assessment, view labelling, and segmentation-based cardiomegaly severity classification. We use the output of the severity classification for large language model-based report generation. Four board-certified radiologists assessed the output accuracy of our CAD pipeline. Across the dataset composed of 377,100 CXR images and 227,827 free-text radiology reports, our system identified 0.18% of cases with mixed-sex mentions, 0.02% of poor quality images (\n                    <jats:italic>F<\/jats:italic>\n                    1\u2009=\u20090.81), and 0.28% of wrongly labelled views (accuracy 99.4%). We assigned views for 4.18% of images which have unlabelled views. Our binary cardiomegaly classification model has 95.2% accuracy. The inter-radiologist agreement on evaluating the generated report\u2019s semantics and correctness for radiologist-MIMIC is 0.62 (strict agreement) and 0.85 (relaxed agreement) similar to the radiologist-CAD agreement of 0.55 (strict) and 0.93 (relaxed). Our work found and corrected several incorrect or missing metadata annotations for the MIMIC-CXR dataset. The performance of our CAD system suggests performance on par with human radiologists. Future improvements revolve around improved text generation and the development of CV tools for other diseases.\n                  <\/jats:p>","DOI":"10.1371\/journal.pdig.0000835","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T13:45:49Z","timestamp":1747748749000},"page":"e0000835","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation"],"prefix":"10.1371","volume":"4","author":[{"given":"Tianhao","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7373-7367","authenticated-orcid":true,"given":"Kexin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wonchan","family":"Son","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristofer","family":"Linton-Reid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Boubnovski-Martell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3881-4870","authenticated-orcid":true,"given":"Matt","family":"Grech-Sollars","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antoine D.","family":"Lain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4971-9003","authenticated-orcid":true,"given":"Joram M.","family":"Posma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"issue":"9","key":"pdig.0000835.ref001","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.1007\/s00330-008-0948-3","article-title":"Digital chest radiography: an update on modern technology, dose containment and control of image quality","volume":"18","author":"C Schaefer-Prokop","year":"2008","journal-title":"Eur Radiol"},{"issue":"529","key":"pdig.0000835.ref002","first-page":"574","article-title":"Chest radiography in general practice: indications, diagnostic yield and consequences for patient management","volume":"56","author":"AM Speets","year":"2006","journal-title":"Br J Gen Pract"},{"issue":"8","key":"pdig.0000835.ref003","first-page":"558","article-title":"Guide to thoracic imaging","volume":"44","author":"S Skinner","year":"2015","journal-title":"Aust Fam Physician"},{"issue":"2","key":"pdig.0000835.ref004","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1148\/radiology.193.2.7972752","article-title":"Chest radiography: estimated lung volume and projected area obscured by the heart, mediastinum, and diaphragm","volume":"193","author":"HG Chotas","year":"1994","journal-title":"Radiology"},{"issue":"5","key":"pdig.0000835.ref005","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1111\/1754-9485.13274","article-title":"Chest radiographs and machine learning\u2014past, present and future","volume":"65","author":"CM Jones","year":"2021","journal-title":"J Med Imaging Radiat Oncol"},{"issue":"8","key":"pdig.0000835.ref006","doi-asserted-by":"crossref","DOI":"10.1016\/j.crad.2022.04.012","article-title":"Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs","volume":"77","author":"MM Boubnovski","year":"2022","journal-title":"Clin Radiol"},{"key":"pdig.0000835.ref007","first-page":"768","article-title":"The radiologist workload increase; where is the limit?: Mini review and case study","volume":"33","author":"V Markotic","year":"2021","journal-title":"Psychiatr Danub"},{"key":"pdig.0000835.ref008","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jacr.2019.07.008","article-title":"Radiologist burnout according to surveyed radiology practice leaders","volume":"17","author":"JR Parikh","year":"2020","journal-title":"J Am Coll Radiol"},{"key":"pdig.0000835.ref009","doi-asserted-by":"crossref","first-page":"110596","DOI":"10.1016\/j.ejrad.2022.110596","article-title":"Radiologist burnout: trends in medical imaging utilization under the national health insurance system with the universal code bundling strategy in an academic tertiary medical centre","volume":"157","author":"YC Peng","year":"2022","journal-title":"Eur J Radiol"},{"issue":"1","key":"pdig.0000835.ref010","first-page":"2168","article-title":"Radiology subspecialisation in Africa: A review of the current status","volume":"25","author":"EP Iyawe","year":"2021","journal-title":"SA J Radiol"},{"issue":"4","key":"pdig.0000835.ref011","doi-asserted-by":"crossref","first-page":"739","DOI":"10.2214\/AJR.16.16963","article-title":"Interpretive error in radiology","volume":"208","author":"S Waite","year":"2017","journal-title":"Am J Roentgenol"},{"issue":"6","key":"pdig.0000835.ref012","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.acra.2018.11.006","article-title":"Perceptual and interpretive error in diagnostic radiology\u2014causes and potential solutions","volume":"26","author":"AJ Degnan","year":"2019","journal-title":"Acad Radiol"},{"issue":"1","key":"pdig.0000835.ref013","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s13244-016-0534-1","article-title":"Error and discrepancy in radiology: inevitable or avoidable?","volume":"8","author":"AP Brady","year":"2017","journal-title":"Insights Imaging"},{"key":"pdig.0000835.ref014","doi-asserted-by":"crossref","first-page":"861680","DOI":"10.3389\/fmed.2022.861680","article-title":"COVID-Net CXR-2: an enhanced deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","volume":"9","author":"M Pavlova","year":"2022","journal-title":"Front Med (Lausanne)"},{"key":"pdig.0000835.ref015","doi-asserted-by":"crossref","first-page":"108826","DOI":"10.1016\/j.patcog.2022.108826","article-title":"Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images","volume":"131","author":"A Sharma","year":"2022","journal-title":"Pattern Recognit"},{"issue":"1","key":"pdig.0000835.ref016","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s12194-017-0394-5","article-title":"Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning","volume":"10","author":"B van Ginneken","year":"2017","journal-title":"Radiol Phys Technol"},{"issue":"6","key":"pdig.0000835.ref017","doi-asserted-by":"crossref","first-page":"6536","DOI":"10.3934\/mbe.2019326","article-title":"Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview","volume":"16","author":"J Gao","year":"2019","journal-title":"Math Biosci Eng"},{"key":"pdig.0000835.ref018","doi-asserted-by":"crossref","first-page":"102382","DOI":"10.1016\/j.artmed.2022.102382","article-title":"CheXGAT: A disease correlation-aware network for thorax disease diagnosis from chest X-ray images","volume":"132","author":"YW Lee","year":"2022","journal-title":"Artif Intell Med"},{"key":"pdig.0000835.ref019","doi-asserted-by":"crossref","first-page":"955765","DOI":"10.3389\/fmed.2022.955765","article-title":"AI-CenterNet CXR: an artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease","volume":"9","author":"S Albahli","year":"2022","journal-title":"Front Med (Lausanne)"},{"issue":"1","key":"pdig.0000835.ref020","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1038\/s41698-024-00502-3","article-title":"Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis","volume":"8","author":"M Boubnovski Martell","year":"2024","journal-title":"NPJ Precis Oncol"},{"issue":"2","key":"pdig.0000835.ref021","doi-asserted-by":"crossref","first-page":"171","DOI":"10.2217\/fca.15.5","article-title":"Cardiothoracic ratio for prediction of left ventricular dilation: a systematic review and pooled analysis","volume":"11","author":"RS Loomba","year":"2015","journal-title":"Future Cardiol"},{"issue":"9","key":"pdig.0000835.ref022","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.3390\/jcm10092016","article-title":"Radiological cardiothoracic ratio in evidence-based medicine","volume":"10","author":"K Truszkiewicz","year":"2021","journal-title":"J Clin Med"},{"key":"pdig.0000835.ref023","article-title":"Cardiomegaly","volume-title":"StatPearls [Internet]","author":"H Amin","year":"2025"},{"issue":"1","key":"pdig.0000835.ref024","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1038\/s41598-024-56079-1","article-title":"Segmentation-based cardiomegaly detection based on semi-supervised estimation of cardiothoracic ratio","volume":"14","author":"P Thiam","year":"2024","journal-title":"Sci Rep"},{"key":"pdig.0000835.ref025","first-page":"482","article-title":"Identifying cardiomegaly in ChestX-ray8 using transfer learning","volume":"264","author":"S Zhou","year":"2019","journal-title":"Stud Health Technol Inform"},{"issue":"12","key":"pdig.0000835.ref026","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1177\/0284185120973630","article-title":"Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods","volume":"62","author":"H Bougias","year":"2021","journal-title":"Acta Radiol"},{"issue":"7","key":"pdig.0000835.ref027","doi-asserted-by":"crossref","first-page":"20584601221107345","DOI":"10.1177\/20584601221107345","article-title":"Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs","volume":"11","author":"P Ajmera","year":"2022","journal-title":"Acta Radiol Open"},{"issue":"3","key":"pdig.0000835.ref028","doi-asserted-by":"crossref","first-page":"871","DOI":"10.3390\/jcm9030871","article-title":"Artificial intelligence-based diagnosis of cardiac and related diseases","volume":"9","author":"M Arsalan","year":"2020","journal-title":"J Clin Med"},{"issue":"1","key":"pdig.0000835.ref029","doi-asserted-by":"crossref","first-page":"16885","DOI":"10.1038\/s41598-021-96433-1","article-title":"Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning","volume":"11","author":"MS Lee","year":"2021","journal-title":"Sci Rep"},{"issue":"6","key":"pdig.0000835.ref030","doi-asserted-by":"crossref","first-page":"988","DOI":"10.3390\/jpm12060988","article-title":"CardioNet: automatic semantic segmentation to calculate the cardiothoracic ratio for cardiomegaly and other chest diseases","volume":"12","author":"A Jafar","year":"2022","journal-title":"J Pers Med"},{"key":"pdig.0000835.ref031","first-page":"3462","article-title":"ChestX-Ray8: Hospital-scale chest X-Ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases","author":"X Wang","year":"2017"},{"issue":"5","key":"pdig.0000835.ref032","doi-asserted-by":"crossref","first-page":"739","DOI":"10.5535\/arm.2012.36.5.739","article-title":"Atypical thoracic solitary plasmacytoma","volume":"36","author":"DH Kim","year":"2012","journal-title":"Ann Rehabil Med"},{"issue":"1","key":"pdig.0000835.ref033","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/s41597-019-0322-0","article-title":"MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports","volume":"6","author":"AEW Johnson","year":"2019","journal-title":"Sci Data"},{"issue":"23","key":"pdig.0000835.ref034","doi-asserted-by":"crossref","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"AL Goldberger","year":"2000","journal-title":"Circulation"},{"issue":"01","key":"pdig.0000835.ref035","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1609\/aaai.v33i01.3301590","article-title":"CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison","volume":"33","author":"J Irvin","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"pdig.0000835.ref036","unstructured":"Peng Y, Wang X, Lu L, Bagheri M, Summers RM, Lu Z. NegBio: a high-performance tool for negation and uncertainty detection in radiology reports. In: AMIA Summits on Translational Science Proceedings. 2018; pp. 188\u201396."},{"key":"pdig.0000835.ref037","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv. preprint. 2015; doi: 10.48550\/ARXIV.1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"pdig.0000835.ref038","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV); 2017. pp. 618\u201326.","DOI":"10.1109\/ICCV.2017.74"},{"key":"pdig.0000835.ref039","doi-asserted-by":"crossref","unstructured":"Seibold C, Jaus A, Fink MA, Kim M, Rei\u00df S, Herrmann K, et al. Accurate fine-grained segmentation of human anatomy in radiographs via volumetric pseudo-labeling. arXiv. preprint. 2023; doi: 10.48550\/ARXIV.2306.03934","DOI":"10.21203\/rs.3.rs-3687730\/v1"},{"issue":"1","key":"pdig.0000835.ref040","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1186\/s12880-022-00827-0","article-title":"Deep transfer learning to quantify pleural effusion severity in chest X-rays","volume":"22","author":"T Huang","year":"2022","journal-title":"BMC Medical Imaging"},{"issue":"1","key":"pdig.0000835.ref041","doi-asserted-by":"crossref","first-page":"20732","DOI":"10.1038\/s41598-022-25027-2","article-title":"Computer-aided diagnosis through medical image retrieval in radiology","volume":"12","author":"W Silva","year":"2022","journal-title":"Sci Rep"},{"issue":"6","key":"pdig.0000835.ref042","article-title":"AI recognition of patient race in medical imaging: a modelling study","volume":"4","author":"JW Gichoya","year":"2022","journal-title":"Lancet Digit Health"},{"key":"pdig.0000835.ref043","doi-asserted-by":"crossref","first-page":"104281","DOI":"10.1016\/j.jbi.2023.104281","article-title":"Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation","volume":"138","author":"Z Lin","year":"2023","journal-title":"J Biomed Inform"},{"key":"pdig.0000835.ref044","doi-asserted-by":"crossref","unstructured":"Zhu Q, Mathai TS, Mukherjee P, Peng Y, Summers RM, Lu Z. Utilizing longitudinal chest X-rays and reports to pre-fill radiology reports. arXiv. preprint. 2023; doi: 10.48550\/ARXIV.2306.08749","DOI":"10.1007\/978-3-031-43904-9_19"},{"issue":"9716","key":"pdig.0000835.ref045","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1016\/S0140-6736(09)62023-7","article-title":"Dilated cardiomyopathy","volume":"375","author":"JL Jefferies","year":"2010","journal-title":"Lancet"},{"issue":"7","key":"pdig.0000835.ref046","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1038\/s41569-018-0007-y","article-title":"Mechanisms of physiological and pathological cardiac hypertrophy","volume":"15","author":"M Nakamura","year":"2018","journal-title":"Nat Rev Cardiol"},{"key":"pdig.0000835.ref047","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.yjmcc.2016.06.001","article-title":"Physiological and pathological cardiac hypertrophy","volume":"97","author":"I Shimizu","year":"2016","journal-title":"J Mol Cell Cardiol"},{"issue":"15","key":"pdig.0000835.ref048","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1056\/NEJM200004133421502","article-title":"Underlying causes and long-term survival in patients with initially unexplained cardiomyopathy","volume":"342","author":"GM Felker","year":"2000","journal-title":"N Engl J Med"},{"issue":"3","key":"pdig.0000835.ref049","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1067\/j.cpradiol.2024.01.011","article-title":"Diagnostic value of cardiothoracic ratio in patients with non-ischaemic cardiomyopathy: comparison to cardiovascular magnetic resonance imaging","volume":"53","author":"R Anjuna","year":"2024","journal-title":"Curr Probl Diagn Radiol"},{"issue":"1","key":"pdig.0000835.ref050","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1186\/s13244-021-01097-0","article-title":"Limitations of cardiothoracic ratio derived from chest radiographs to predict real heart size: comparison with magnetic resonance imaging","volume":"12","author":"P Simkus","year":"2021","journal-title":"Insights Imaging"},{"key":"pdig.0000835.ref051","first-page":"768","article-title":"The radiologist workload increase; where is the limit?: Mini review and case study","volume":"33","author":"V Markotic","year":"2021","journal-title":"Psychiatr Danub"},{"key":"pdig.0000835.ref052","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR52729.2023.00718","article-title":"Interactive and explainable region-guided radiology report generation","author":"T Tanida","year":"2023"},{"key":"pdig.0000835.ref053","doi-asserted-by":"crossref","first-page":"102633","DOI":"10.1016\/j.artmed.2023.102633","article-title":"Improving chest X-ray report generation by leveraging warm starting","volume":"144","author":"A Nicolson","year":"2023","journal-title":"Artif Intell Med"},{"key":"pdig.0000835.ref054","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2023.bionlp-1.45","article-title":"Overview of the RadSum23 shared task on multi-modal and multi-anatomical radiology report summarization","author":"JB Delbrouck","year":"2023"},{"issue":"8","key":"pdig.0000835.ref055","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.mpmed.2018.05.007","article-title":"The chest radiograph in heart disease","volume":"46","author":"MT Tsakok","year":"2018","journal-title":"Medicine"},{"issue":"1","key":"pdig.0000835.ref056","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10554-018-1442-x","article-title":"Is there any diagnostic value of anteroposterior chest radiography in predicting cardiac chamber enlargement?","volume":"35","author":"H Sahin","year":"2018","journal-title":"Int J Cardiovasc Imaging"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000835","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T13:46:04Z","timestamp":1747748764000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000835"}},"subtitle":[],"editor":[{"given":"Hualou","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,5,20]]},"references-count":56,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5,20]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0000835","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.09.02.24311997","asserted-by":"object"}]},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,20]]}}}