{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T18:45:18Z","timestamp":1789843518807,"version":"4.0.1"},"reference-count":40,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Chest X-rays (CXRs) are pivotal in clinical diagnostics, particularly in assessing cardiomegaly through the cardiothoracic ratio (CTR). This systematic review and meta-analysis evaluate the efficacy of artificial intelligence (AI) in automating CTR determination to enhance patient care and streamline diagnostic processes. They are concentrated on comparing the performance of AI models in determining the CTR against human assessments, identifying the most effective models for potential clinical implementation. This study was registered with PROSPERO (no. CRD42023437459). No funding was received. Methods: A comprehensive search of medical databases was conducted in June 2023. The search strategy adhered to the PICO framework. Inclusion criteria encompassed original articles from the last decade focusing on AI-assisted CTR assessment from standing-position CXRs. Exclusion criteria included systematic reviews, meta-analyses, conference abstracts, paediatric studies, non-original articles, and studies using imaging techniques other than X-rays. After initial screening, 117 articles were reviewed, with 14 studies meeting the final inclusion criteria. Data extraction was performed by three independent investigators, and quality assessment followed PRISMA 2020 guidelines, using tools such as the JBI Checklist, AMSTAR 2, and CASP Diagnostic Study Checklist. Risk of bias was assessed according to the Cochrane Handbook guidelines. Results: Fourteen studies, comprising a total of 70,472 CXR images, met the inclusion criteria. Various AI models were evaluated, with differences in dataset characteristics and AI technology used. Common preprocessing techniques included resizing and normalization. The pooled AUC for cardiomegaly detection was 0.959 (95% CI 0.944\u20130.975). The pooled standardized mean difference for CTR measurement was 0.0353 (95% CI 0.147\u20130.0760). Significant heterogeneity was found between studies (I2 89.97%, p &lt; 0.0001), with no publication bias detected. Conclusions: Standardizing methodologies is crucial to avoid interpretational errors and advance AI in medical imaging diagnostics. Uniform reporting standards are essential for the further development of AI in CTR measurement and broader medical imaging applications.<\/jats:p>","DOI":"10.3390\/jcm13164659","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T15:04:24Z","timestamp":1723129464000},"page":"4659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Measurement of Cardiothoracic Ratio on Chest X-rays Using Artificial Intelligence\u2014A Systematic Review and Meta-Analysis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7633-3600","authenticated-orcid":false,"given":"Jakub","family":"Kufel","sequence":"first","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Medyk\u00f3w 14, 40-752 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3886-9708","authenticated-orcid":false,"given":"\u0141ukasz","family":"Czogalik","sequence":"additional","affiliation":[{"name":"Students\u2019 Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Medical University of Silesia in Katowice, 40-752 Katowice, Poland"},{"name":"Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0609-1678","authenticated-orcid":false,"given":"Micha\u0142","family":"Biel\u00f3wka","sequence":"additional","affiliation":[{"name":"Students\u2019 Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Medical University of Silesia in Katowice, 40-752 Katowice, Poland"},{"name":"Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9952-9681","authenticated-orcid":false,"given":"Miko\u0142aj","family":"Magiera","sequence":"additional","affiliation":[{"name":"Students\u2019 Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Medical University of Silesia in Katowice, 40-752 Katowice, Poland"},{"name":"Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3383-6477","authenticated-orcid":false,"given":"Adam","family":"Mitr\u0119ga","sequence":"additional","affiliation":[{"name":"Students\u2019 Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Medical University of Silesia in Katowice, 40-752 Katowice, Poland"},{"name":"Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piotr","family":"Dudek","sequence":"additional","affiliation":[{"name":"Students\u2019 Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Medical University of Silesia in Katowice, 40-752 Katowice, Poland"},{"name":"Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarzyna","family":"Bargie\u0142-\u0141\u0105czek","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, Szpital Specjalistyczny im. Sz. Starkiewicza, 41-300 D\u0105browa G\u00f3rnicza, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magdalena","family":"Stencel","sequence":"additional","affiliation":[{"name":"Students\u2019 Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Medical University of Silesia in Katowice, 40-752 Katowice, Poland"},{"name":"Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wiktoria","family":"Bartnikowska","sequence":"additional","affiliation":[{"name":"Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-752 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4228-9343","authenticated-orcid":false,"given":"Sylwia","family":"Mielcarska","sequence":"additional","affiliation":[{"name":"Department of Medical and Molecular Biology, Faculty of Medical Sciences, Medical University of Silesia, 41-808 Zabrze, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7536-9775","authenticated-orcid":false,"given":"Sandra","family":"Modli\u0144ska","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Medyk\u00f3w 14, 40-752 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0638-3789","authenticated-orcid":false,"given":"Zbigniew","family":"Nawrat","sequence":"additional","affiliation":[{"name":"Foundation of Cardiac Surgery Development, 41-800 Zabrze, Poland"},{"name":"Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4917-8526","authenticated-orcid":false,"given":"Maciej","family":"Cebula","sequence":"additional","affiliation":[{"name":"Individual Medical Practice Maciej Cebula, 40-754 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarzyna","family":"Gruszczy\u0144ska","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Medyk\u00f3w 14, 40-752 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"ref_1","first-page":"574","article-title":"Chest Radiography in General Practice: Indications, Diagnostic Yield and Consequences for Patient Management","volume":"56","author":"Speets","year":"2006","journal-title":"Br. J. Gen. Pract."},{"key":"ref_2","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":"Tsakok","year":"2018","journal-title":"Medicine"},{"key":"ref_3","unstructured":"Arzhaeva, Y., Wang, D., Devnath, L., Amirgholipour, S., McBean, R., Hillhouse, J., Luo, S., Meredith, D., Newbigin, K., and Yates, D. (2019). Development of Automated Diagnostic Tools for Pneumoconiosis Detection from Chest X-ray Radiographs, CSIRO. Available online: https:\/\/www.coalservices.com.au\/wp-content\/uploads\/2023\/06\/Project-No.-20647-Final-Report.pdf."},{"key":"ref_4","unstructured":"Murphy, K. (2019). How Data Will Improve Healthcare without Adding Staff or Beds. The Global Innovation Index 2019, World Intellectual Property Organization. Available online: https:\/\/www.wipo.int\/edocs\/pubdocs\/en\/wipo_pub_gii_2019-chapter8.pdf."},{"key":"ref_5","unstructured":"Sharma, R., and Gaillard, F. (2024, January 24). Cardiothoracic Ratio. Radiopaedia. Available online: https:\/\/radiopaedia.org\/articles\/cardiothoracic-ratio?lang=us."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/0720-048X(92)90080-S","article-title":"Cardiac Size in the Supine Chestfilm","volume":"14","author":"Smits","year":"1992","journal-title":"Eur. J. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Truszkiewicz, K., Por\u0119ba, R., and Ga\u0107, P. (2021). Radiological Cardiothoracic Ratio in Evidence-Based Medicine. J. Clin. Med., 10.","DOI":"10.3390\/jcm10092016"},{"key":"ref_8","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":"Simkus","year":"2021","journal-title":"Insights Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/nrcardio.2010.165","article-title":"Epidemiology and Risk Profile of Heart Failure","volume":"8","author":"Bui","year":"2011","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_10","unstructured":"Amin, H., and Siddiqui, W.J. (2024). Cardiomegaly. StatPearls, StatPearls Publishing."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Saiviroonporn, P., Rodbangyang, K., Tongdee, T., Chaisangmongkon, W., Yodprom, P., Siriapisith, T., Wonglaksanapimon, S., and Thiravit, P. (2021). Cardiothoracic Ratio Measurement Using Artificial Intelligence: Observer and Method Validation Studies. BMC Med. Imaging, 21.","DOI":"10.1186\/s12880-021-00625-0"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1007\/s00405-023-08219-y","article-title":"Validity and Reliability of an Instrument Evaluating the Performance of Intelligent Chatbot: The Artificial Intelligence Performance Instrument (AIPI)","volume":"281","author":"Lechien","year":"2024","journal-title":"Eur. Arch. Otorhinolaryngol."},{"key":"ref_13","unstructured":"Kufel, J., Biel\u00f3wka, M., and Mitr\u0119ga, A. (2024, February 04). Detection and Measurement of Cardiothoracic Ratio on Chest X-rays Using Artificial Intelligence\u2014A Systematic Review and Meta-Analysis. PROSPERO 2023 CRD42023437459. Available online: https:\/\/www.crd.york.ac.uk\/prospero\/display_record.php?ID=CRD42023437459."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Schardt, C., Adams, M.B., Owens, T., Keitz, S., and Fontelo, P. (2007). Utilization of the PICO Framework to Improve Searching PubMed for Clinical Questions. BMC Med. Inform. Decis. Mak., 7.","DOI":"10.1186\/1472-6947-7-16"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1186\/s13643-016-0384-4","article-title":"Rayyan\u2014A Web and Mobile App for Systematic Reviews","volume":"5","author":"Ouzzani","year":"2016","journal-title":"Syst. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Interrater Reliability: The Kappa Statistic","volume":"22","author":"McHugh","year":"2012","journal-title":"Biochem. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"n160","DOI":"10.1136\/bmj.n160","article-title":"PRISMA 2020 Explanation and Elaboration: Updated Guidance and Exemplars for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1097\/XEB.0000000000000055","article-title":"Summarizing Systematic Reviews: Methodological Development, Conduct and Reporting of an Umbrella Review Approach","volume":"13","author":"Aromataris","year":"2015","journal-title":"Int. J. Evid. -Based Healthc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"j4008","DOI":"10.1136\/bmj.j4008","article-title":"AMSTAR 2: A Critical Appraisal Tool for Systematic Reviews That Include Randomised or Non-Randomised Studies of Healthcare Interventions, or Both","volume":"358","author":"Shea","year":"2017","journal-title":"BMJ"},{"key":"ref_20","first-page":"31","article-title":"Optimising the Value of the Critical Appraisal Skills Programme (CASP) Tool for Quality Appraisal in Qualitative Evidence Synthesis","volume":"1","author":"Long","year":"2020","journal-title":"Res. Methods Med. Health Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"d5928","DOI":"10.1136\/bmj.d5928","article-title":"The Cochrane Collaboration\u2019s Tool for Assessing Risk of Bias in Randomised Trials","volume":"343","author":"Higgins","year":"2011","journal-title":"BMJ"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Owais, M., Mahmood, T., Choi, J., and Park, K.R. (2020). Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases. J. Clin. Med., 9.","DOI":"10.3390\/jcm9030871"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chou, H.-H., Lin, J.-Y., Shen, G.-T., and Huang, C.-Y. (2023). Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients. Diagnostics, 13.","DOI":"10.3390\/diagnostics13081376"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8130","DOI":"10.1007\/s00330-021-07963-1","article-title":"Automatic Prediction of Left Cardiac Chamber Enlargement from Chest Radiographs Using Convolutional Neural Network","volume":"31","author":"Nam","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110287","DOI":"10.1109\/ACCESS.2021.3101253","article-title":"External Validation of Deep Learning Algorithms for Cardiothoracic Ratio Measurement","volume":"9","author":"Chaisangmongkon","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e200172","DOI":"10.1148\/ryai.2021200172","article-title":"Detection and Semiquantitative Analysis of Cardiomegaly, Pneumothorax, and Pleural Effusion on Chest Radiographs","volume":"3","author":"Zhou","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"37749","DOI":"10.1109\/ACCESS.2019.2900053","article-title":"Automatic Cardiothoracic Ratio Calculation with Deep Learning","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"47824","DOI":"10.1109\/ACCESS.2022.3171811","article-title":"Chest X-Ray Image Analysis with Combining 2D and 1D Convolutional Neural Network Based Classifier for Rapid Cardiomegaly Screening","volume":"10","author":"Wu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lee, M.S., Kim, Y.S., Kim, M., Usman, M., Byon, S.S., Kim, S.H., Lee, B.I., and Lee, B.-D. (2021). Evaluation of the Feasibility of Explainable Computer-Aided Detection of Cardiomegaly on Chest Radiographs Using Deep Learning. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-96433-1"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s43856-023-00241-9","article-title":"Cardiothoracic Ratio Values and Trajectories Are Associated with Risk of Requiring Dialysis and Mortality in Chronic Kidney Disease","volume":"3","author":"Chou","year":"2023","journal-title":"Commun. Med."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jafar, A., Hameed, M.T., Akram, N., Waqas, U., Kim, H.S., and Naqvi, R.A. (2022). CardioNet: Automatic Semantic Segmentation to Calculate the Cardiothoracic Ratio for Cardiomegaly and Other Chest Diseases. J. Pers. Med., 12.","DOI":"10.3390\/jpm12060988"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Saiviroonporn, P., Wonglaksanapimon, S., Chaisangmongkon, W., Chamveha, I., Yodprom, P., Butnian, K., Siriapisith, T., and Tongdee, T. (2022). A Clinical Evaluation Study of Cardiothoracic Ratio Measurement Using Artificial Intelligence. BMC Med. Imaging, 22.","DOI":"10.1186\/s12880-022-00767-9"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"205846012211073","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":"Ajmera","year":"2022","journal-title":"Acta Radiol. Open"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Que, Q., Tang, Z., Wang, R., Zeng, Z., Wang, J., Chua, M., Gee, T.S., Yang, X., and Veeravalli, B. (2018, January 18\u201321). CardioXNet: Automated Detection for Cardiomegaly Based on Deep Learning. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512374"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Candemir, S., Rajaraman, S., Thoma, G., and Antani, S. (2018, January 28\u201330). Deep Learning for Grading Cardiomegaly Severity in Chest X-Rays: An Investigation. Proceedings of the 2018 IEEE Life Sciences Conference (LSC), Montreal, QC, Canada.","DOI":"10.1109\/LSC.2018.8572113"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1002\/clc.24105","article-title":"The Diagnostic Value of Chest X-ray Scanning by the Help of Artificial Intelligence in Heart Failure (ART-IN-HF)","volume":"46","author":"Celik","year":"2023","journal-title":"Clin. Cardiol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kim, D., Lee, J.H., Jang, M., Park, J., Hong, W., Lee, C.S., Yang, S.Y., and Park, C.M. (2023). The Performance of a Deep Learning-Based Automatic Measurement Model for Measuring the Cardiothoracic Ratio on Chest Radiographs. Bioengineering, 10.","DOI":"10.3390\/bioengineering10091077"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Maniaci, A., Riela, P.M., Iannella, G., Lechien, J.R., La Mantia, I., De Vincentiis, M., Cammaroto, G., Calvo-Henriquez, C., Di Luca, M., and Chiesa Estomba, C. (2023). Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study. Life, 13.","DOI":"10.3390\/life13030702"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Whiting, P., Rutjes, A.W.S., Reitsma, J.B., Bossuyt, P.M.M., and Kleijnen, J. (2003). The development of QUADAS: A tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med. Res. Methodol., 3.","DOI":"10.1186\/1471-2288-3-25"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/16\/4659\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:32:25Z","timestamp":1760110345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/16\/4659"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,8]]},"references-count":40,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["jcm13164659"],"URL":"https:\/\/doi.org\/10.3390\/jcm13164659","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,8]]}}}