{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:21:41Z","timestamp":1778347301563,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Healthcare"],"abstract":"<jats:p>Statistics show that an estimated 64 million people worldwide suffer from glaucoma. To aid in the detection of this disease, this paper presents a new public dataset containing eye fundus images that was developed for glaucoma pattern-recognition studies using deep learning (DL). The dataset, denoted Brazil Glaucoma, comprises 2000 images obtained from 1000 volunteers categorized into two groups: those with glaucoma (50%) and those without glaucoma (50%). All images were captured with a smartphone attached to a Welch Allyn panoptic direct ophthalmoscope. Further, a DL approach for the automatic detection of glaucoma was developed using the new dataset as input to a convolutional neural network ensemble model. The accuracy between positive and negative glaucoma detection, sensitivity, and specificity were calculated using five-fold cross-validation to train and refine the classification model. The results showed that the proposed method can identify glaucoma from eye fundus images with an accuracy of 90.0%. Thus, the combination of fundus images obtained using a smartphone attached to a portable panoptic ophthalmoscope and artificial intelligence algorithms yielded satisfactory results in the overall accuracy of glaucoma detection tests. Consequently, the proposed approach can contribute to the development of technologies aimed at massive population screening of the disease.<\/jats:p>","DOI":"10.3390\/healthcare10122345","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T02:55:51Z","timestamp":1669172151000},"page":"2345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Detection of Glaucoma on Fundus Images Using Deep Learning on a New Image Set Obtained with a Smartphone and Handheld Ophthalmoscope"],"prefix":"10.3390","volume":"10","author":[{"given":"Clerimar Paulo","family":"Bragan\u00e7a","sequence":"first","affiliation":[{"name":"ISUS Unit, Faculdade de Ci\u00eancia e Tecnologia, Universidade Fernando Pessoa, 4249-004 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8280-1324","authenticated-orcid":false,"given":"Jos\u00e9 Manuel","family":"Torres","sequence":"additional","affiliation":[{"name":"ISUS Unit, Faculdade de Ci\u00eancia e Tecnologia, Universidade Fernando Pessoa, 4249-004 Porto, Portugal"},{"name":"Artificial Intelligence and Computer Science Laboratory, LIACC, University of Porto, 4100-000 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0382-879X","authenticated-orcid":false,"given":"Christophe Pinto de Almeida","family":"Soares","sequence":"additional","affiliation":[{"name":"ISUS Unit, Faculdade de Ci\u00eancia e Tecnologia, Universidade Fernando Pessoa, 4249-004 Porto, Portugal"},{"name":"Artificial Intelligence and Computer Science Laboratory, LIACC, University of Porto, 4100-000 Porto, Portugal"}]},{"given":"Luciano Oliveira","family":"Macedo","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Eye Hospital of Southern Minas Gerais State, R. Joaquim Rosa, 14, Itanhandu 37464-000, MG, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022, November 10). World Report on Vision, Available online: https:\/\/apps.who.int\/iris\/handle\/10665\/328717."},{"key":"ref_2","unstructured":"Kanski, J.J. (2015). Clinical Ophthalmology: A Systematic Approach, Elsevier. [6th ed.]."},{"key":"ref_3","first-page":"225","article-title":"The diagnosis and treatment of glaucoma","volume":"117","author":"Schuster","year":"2020","journal-title":"Dtsch. Arztebl. Int."},{"key":"ref_4","unstructured":"Muir, K.W., and Chen, T.C. (2022). Glaucoma 2022: Second-to-None Glaucoma Care from the Second City. Am. Acad. Ophthalmol., Available online: https:\/\/www.aao.org\/Assets\/497788e8-b360-40d1-9b57-1882116588ea\/637993593269530000\/glaucoma-2022-syllabus-pdf?inline=1."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pachade, S., Porwal, P., Thulkar, D., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., Giancardo, L., Quellec, G., and M\u00e9riaudeau, F. (2021). Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research. Data, 6.","DOI":"10.3390\/data6020014"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Murthi, A., and Madheswaran, M. (2012, January 10\u201312). Enhancement of optic cup to disc ratio detection in glaucoma diagnosis. Proceedings of the 2012 International Conference on Computer Communication and Informatics, Coimbatore, India.","DOI":"10.1109\/ICCCI.2012.6158789"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Neto, A., Camara, J., and Cunha, A. (2022). Evaluations of deep learning approaches for glaucoma screening using retinal images from mobile device. Sensors, 22.","DOI":"10.3390\/s22041449"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106","DOI":"10.5005\/jp-journals-10008-1146","article-title":"Evaluation of the optic nerve head in glaucoma","volume":"7","author":"Gandhi","year":"2013","journal-title":"J. Curr. Glaucoma Pract."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1136\/bjo.2005.067355","article-title":"Direct costs of glaucoma and severity of the disease: A multinational long term study of resource utilization in Europe","volume":"89","author":"Traverso","year":"2005","journal-title":"Br. J. Ophthalmol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1177\/1355819613499748","article-title":"Is it worthwhile to conduct a randomized controlled trial of glaucoma screening in the United Kingdom?","volume":"19","author":"Burr","year":"2014","journal-title":"J. Health Serv. Res. Policy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1111\/j.1755-3768.2007.00947.x","article-title":"Cost effectiveness and cost utility of an organized screening programme for glaucoma","volume":"85","author":"Tuulonen","year":"2007","journal-title":"Acta. Ophthalmol. Scand."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Imran, A., Li, J., Pei, Y., Akhtar, F., Yang, J.-J., and Wang, Q. (2019, January 6\u20139). Cataract Detection and Grading with Retinal Images Using SOM-RBF Neural Network. Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China.","DOI":"10.1109\/SSCI44817.2019.9002864"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2407","DOI":"10.1007\/s00371-020-01994-3","article-title":"Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network","volume":"37","author":"Imran","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_14","first-page":"691","article-title":"Automated identification of cataract severity using retinal fundus images","volume":"8","author":"Imran","year":"2020","journal-title":"Comput. Methods Biomech. Biomed. Eng.: Imaging Vis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","article-title":"Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis","volume":"121","author":"Tham","year":"2014","journal-title":"Ophthalmology"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41433-019-0577-x","article-title":"The impact of artificial intelligence in the diagnosis and management of glaucoma","volume":"34","author":"Mayro","year":"2020","journal-title":"Eye"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"823139","DOI":"10.1155\/2015\/823139","article-title":"A novel device to exploit the smartphone camera for fundus photography","volume":"2015","author":"Russo","year":"2015","journal-title":"J. Ophthalmol."},{"key":"ref_18","unstructured":"(2022, February 20). PanOptic, Panoptic + Iexaminer. Available online: http:\/\/www.welchallyn.com\/en\/microsites\/iexaminer.html\/."},{"key":"ref_19","unstructured":"Volk (2022, February 20). Volk Optical in View. Available online: https:\/\/www.volk.com\/collections\/diagnostic-imaging\/products\/inview-for-iphone-6-6s.html\/."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12938-019-0649-y","article-title":"CNNs for automatic glaucoma assessment using fundus images: An ex-tensive validation","volume":"18","author":"Morales","year":"2019","journal-title":"Biomed. Eng. Online"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.artmed.2008.04.005","article-title":"Identification of the optic nerve head with genetic algorithms","volume":"43","author":"Carmona","year":"2008","journal-title":"Artif. Intell. Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sivaswamy, J., Krishnadas, S.R., Datt Joshi, G., Jain, M., and Syed Tabish, A.U. (May, January 29). Drishti-Gs: Retinal image dataset for optic nerve head (ONH) segmentation. Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, China.","DOI":"10.1109\/ISBI.2014.6867807"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge-based vessel segmentation in color images of the retina","volume":"23","author":"Staal","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1049\/iet-ipr.2016.0812","article-title":"Improved auto-mated detection of glaucoma from fundus image using hybrid structural and textural features","volume":"11","author":"Khalil","year":"2017","journal-title":"IET Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"154860","DOI":"10.1155\/2013\/154860","article-title":"Robust vessel segmentation in fundus images","volume":"2013","author":"Budai","year":"2013","journal-title":"Int. J. Biomed. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a publicly distributed image database: The Messidor database","volume":"33","author":"Zhang","year":"2014","journal-title":"Image Anal. Stereol."},{"key":"ref_27","unstructured":"Zhang, Z., Yin, F.S., Liu, J., Wong, W.K., Tan, N.M., Lee, B.H., Cheng, J., and Wong, T.Y. (September, January 31). Origa-light: An online retinal fundus image database for glaucoma analysis and research. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC\u201910, Buenos Aires, Argentina."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1038\/s41597-022-01388-1","article-title":"Origa-light: An online retinal fundus image database for glaucoma analysis and research, PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment","volume":"9","author":"Kovalyk","year":"2022","journal-title":"Sci. Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101570","DOI":"10.1016\/j.media.2019.101570","article-title":"Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs","volume":"59","author":"Orlando","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"161","DOI":"10.5566\/ias.2346","article-title":"Rim-One Dl: A unified retinal image database for assessing glaucoma using deep learning","volume":"39","author":"Fumero","year":"2020","journal-title":"Image Anal. Stereol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fumero, F., Alayon, S., Sanchez, J.L., Sigut, J., and Gonzalez-Hernandez, M. (2011, January 27\u201330). Rim-one: An open retinal image database for optic nerve evaluation. Proceedings of the 2011 24th International Symposium on Computer-Based MedicalSystems (CBMS), Bristol, UK.","DOI":"10.1109\/CBMS.2011.5999143"},{"key":"ref_32","unstructured":"Fumero, F., Sigut, J., Alay\u00f3n, S., Gonz\u00e1lez-Hern\u00e1ndez, M., and Gonz\u00e1lez De La Rosa, M. (2015, January 8\u201312). Interactive tool and database for optic disc and cup segmentation of stereo and monocular retinal fundus images. Proceedings of the 23rd Conference on Computer Graphics, Visualization and Computer Vision 2015, Plzen, Czech Republic."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bajwa, M.N., Singh, G., Neumeier, W., Malik, M.I., Dengel, A., and Ahmed, S. (2020). G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection. arXiv.","DOI":"10.1109\/IJCNN48605.2020.9207664"},{"key":"ref_34","first-page":"100038","article-title":"Glaucoma detection in retinal fundus images using u-net and supervised machine learning algorithms","volume":"5","author":"Shinde","year":"2021","journal-title":"Intell-Based Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sreng, S., Maneerat, N., Hamamoto, K., and Win, K.Y. (2020). Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images. Appl. Sci., 10.","DOI":"10.3390\/app10144916"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10278-019-00189-0","article-title":"Glaucoma detection from retinal images using statistical and textural wavelet features","volume":"33","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s11517-020-02307-5","article-title":"An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus","volume":"59","author":"Singh","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_38","first-page":"669","article-title":"Automatic feature learning for glaucoma detection based on deep learning","volume":"9351","author":"Chen","year":"2015","journal-title":"MICCAI"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ins.2018.01.051","article-title":"Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images","volume":"441","author":"Raghavendra","year":"2018","journal-title":"J. Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102597","DOI":"10.1016\/j.jvcir.2019.102597","article-title":"An hybrid feature space from texture information and trans-fer learning for glaucoma classification","volume":"64","author":"Claro","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.eswa.2018.06.010","article-title":"Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma","volume":"110","author":"Gattass","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Aamir, M., Irfan, M., Ali, T., Ali, G., Shaf, A., Saeed S, A., Al-Beshri, A., Alasbali, T., and Mahnashi, M.H. (2020). An Adoptive Threshold-Based Multi-Level Deep Convolutional Neural Network for Glaucoma Eye Disease Detection and Classification. Diagnostics, 10.","DOI":"10.3390\/diagnostics10080602"},{"key":"ref_43","unstructured":"Goodfellow, A.C.I.J., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1109\/TMI.2019.2927226","article-title":"A large-scale database and a CNN model for attention-based glaucoma detection","volume":"39","author":"Li","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.ophtha.2018.01.023","article-title":"Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs","volume":"8","author":"Li","year":"2018","journal-title":"Ophthalmology"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ogla.2018.04.002","article-title":"A deep learning-based algorithm identifies glaucomatous discs using monoscopic fundus photographs, ophthalmology","volume":"1","author":"Liu","year":"2018","journal-title":"Glaucoma"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1109\/TMI.2018.2837012","article-title":"Disc-aware ensemble network for glaucoma screening from fundus image","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"14665","DOI":"10.1038\/s41598-018-33013-w","article-title":"Development of a deep residual learning algorithm to screen for glaucoma from fundus photography","volume":"8","author":"Shibata","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Norouzifard, M., Nemati, A., GholamHosseini, H., Klette, R., Nouri-Mahdavi, K., and Yousefi, S. (2018, January 19\u201321). Automated glaucoma diagnosis using deep and transfer learning: Proposal of a system for clinical testing. Proceedings of the 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, New Zealand.","DOI":"10.1109\/IVCNZ.2018.8634671"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"16685","DOI":"10.1038\/s41598-018-35044-9","article-title":"Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs","volume":"1","author":"Christopher","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_53","unstructured":"HO (2022, June 10). Eye Hospital of the South of the State of Minas Gerais. Available online: https:\/\/new.hosuldeminas.com.br\/."},{"key":"ref_54","unstructured":"SUS (2022, June 11). Sistema \u00danico de Sa\u00fade (sus), Available online: https:\/\/www.gov.br\/saude\/pt-br\/assuntos\/saude-de-a-a-z\/s\/sus-estrutura-principios-e-como-funciona."},{"key":"ref_55","unstructured":"da Sa\u00fade, M. (2022, June 11). Protocolo Cl\u00ednico e Diretrizes Terap\u00eauticas do Glaucoma-Portaria No. 1279, Available online: http:\/\/conitec.gov.br\/images\/Consultas\/Relatorios\/2022\/20220325_Relatorio_PCDT_do_Glaucoma_CP_09.pdf."},{"key":"ref_56","unstructured":"(2022, June 11). Applications of Deep Neural Networks. Available online: https:\/\/arxiv.org\/abs\/2009.05673v1."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. arXiv.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_58","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017). Inception-v4, inception-ResNet and the impact of residual connections on learning. arXiv.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity mappings in deep residual networks. arXiv.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2016). Denselyconnected convolutional networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_63","first-page":"1800","article-title":"Xception: Deep learning with depthwise separable convolutions","volume":"4","author":"Chollet","year":"2017","journal-title":"IEEE Comput. Soc."},{"key":"ref_64","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1136\/ebnurs-2019-103225","article-title":"What are sensitivity and specificity?","volume":"23","author":"Swift","year":"2020","journal-title":"Evid. Based. Nurs."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, Sage Publications.","DOI":"10.1177\/001316446002000104"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1136\/emermed-2017-206735","article-title":"What is an ROC curve?","volume":"34","author":"Hoo","year":"2017","journal-title":"Emerg Med. J."}],"container-title":["Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9032\/10\/12\/2345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:24:24Z","timestamp":1760145864000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9032\/10\/12\/2345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,22]]},"references-count":67,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["healthcare10122345"],"URL":"https:\/\/doi.org\/10.3390\/healthcare10122345","relation":{},"ISSN":["2227-9032"],"issn-type":[{"value":"2227-9032","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,22]]}}}