{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T20:34:40Z","timestamp":1784666080601,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T00:00:00Z","timestamp":1697068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research to Prevent Blindness to the Gavin Herbert Eye Institute at the University of California"},{"name":"Research to Prevent Blindness to the Department of Ophthalmology at the University of Rochester"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI\u2019s potential in diagnosing etiologies of pediatric retinal hemorrhages.<\/jats:p>","DOI":"10.3390\/ijms242015105","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T03:07:47Z","timestamp":1697080067000},"page":"15105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3631-4760","authenticated-orcid":false,"given":"Pooya","family":"Khosravi","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA"},{"name":"Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA"},{"name":"Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8164-6695","authenticated-orcid":false,"given":"Nolan A.","family":"Huck","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA"},{"name":"Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6448-6351","authenticated-orcid":false,"given":"Kourosh","family":"Shahraki","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA"},{"name":"Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen C.","family":"Hunter","sequence":"additional","affiliation":[{"name":"School of Medicine, University of California, 900 University Ave, Riverside, CA 92521, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clifford Neil","family":"Danza","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA"},{"name":"Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"So Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, College of Medicine, Soonchunhyang University, Cheonan 31151, Chungcheongnam-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian J.","family":"Forbes","sequence":"additional","affiliation":[{"name":"Division of Ophthalmology, Children\u2019s Hospital of Philadelphia, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuan","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Queensland Children\u2019s Hospital, South Brisbane, QLD 4101, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex V.","family":"Levin","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Flaum Eye Institute, Golisano Children\u2019s Hospital, Rochester, NY 14642, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gil","family":"Binenbaum","sequence":"additional","affiliation":[{"name":"Division of Ophthalmology, Children\u2019s Hospital of Philadelphia, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter D.","family":"Chang","sequence":"additional","affiliation":[{"name":"Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA"},{"name":"Department of Radiological Sciences, School of Medicine, University of California, Irvine, CA 92697, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0348-2060","authenticated-orcid":false,"given":"Donny W.","family":"Suh","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA"},{"name":"Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104901","DOI":"10.1016\/j.chiabu.2020.104901","article-title":"Retinal hemorrhage and bleeding disorders in children: A review","volume":"112","author":"Thau","year":"2021","journal-title":"Child Abus. Negl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Di Fazio, N., Delogu, G., Morena, D., Cipolloni, L., Scopetti, M., Mazzilli, S., Frati, P., and Fineschi, V. (2023). New Insights into the Diagnosis and Age Determination of Retinal Hemorrhages from Abusive Head Trauma: A Systematic Review. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101722"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.jaapos.2016.03.001","article-title":"Retinal hemorrhage: Science versus speculation","volume":"20","author":"Levin","year":"2016","journal-title":"J. Am. Assoc. Pediatr. Ophthalmol. Strabismus"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1159\/000360469","article-title":"Subretinal Hemorrhage","volume":"54","author":"Yiu","year":"2014","journal-title":"Dev. Ophthalmol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.jaapos.2010.03.002","article-title":"Evaluation and management of retinal hemorrhages in infants with and without abusive head trauma","volume":"14","author":"Forbes","year":"2010","journal-title":"J. Am. Assoc. Pediatr. Ophthalmol. Strabismus"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jaapos.2012.07.012","article-title":"Newborn retinal hemorrhages: A systematic review","volume":"17","author":"Watts","year":"2013","journal-title":"J. Am. Assoc. Pediatr. Ophthalmol. Strabismus"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e154","DOI":"10.3928\/23258160-20181002-20","article-title":"Terson Syndrome in a Healthy Term Infant: Delivery-Associated Retinopathy and Intracranial Hemorrhage","volume":"49","author":"Moshfeghi","year":"2018","journal-title":"Ophthalmic Surg. Lasers Imaging Retin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1542\/peds.114.1.165","article-title":"Characteristics That Distinguish Accidental from Abusive Injury in Hospitalized Young Children with Head Trauma","volume":"114","author":"Bechtel","year":"2004","journal-title":"Pediatrics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"253","DOI":"10.3341\/kjo.2021.0133","article-title":"Exploring the Vitreoretinal Interface: A Key Instigator of Unique Retinal Hemorrhage Patterns in Pediatric Head Trauma","volume":"36","author":"Song","year":"2022","journal-title":"Korean J. Ophthalmol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1111\/j.1442-9071.2010.02291.x","article-title":"Retinal haemorrhage in abusive head trauma","volume":"38","author":"Morad","year":"2010","journal-title":"Clin. Exp. Ophthalmol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.jemermed.2008.06.022","article-title":"Retinal Hemorrhages and Shaken Baby Syndrome: An Evidence-Based Review","volume":"37","author":"Togioka","year":"2008","journal-title":"J. Emerg. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.jaapos.2012.03.004","article-title":"Central retinal artery occlusion in Crohn disease","volume":"16","author":"Falavarjani","year":"2012","journal-title":"J. Am. Assoc. Pediatr. Ophthalmol. Strabismus"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.jaapos.2015.12.008","article-title":"The natural history of retinal hemorrhage in pediatric head trauma","volume":"20","author":"Binenbaum","year":"2016","journal-title":"J. Am. Assoc. Pediatr. Ophthalmol. Strabismus"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101758","DOI":"10.1016\/j.artmed.2019.101758","article-title":"Ophthalmic diagnosis using deep learning with fundus images\u2014A critical review","volume":"102","author":"Sengupta","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_15","first-page":"264","article-title":"Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology","volume":"8","author":"Balyen","year":"2019","journal-title":"Asia-Pacific J. Ophthalmol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100759","DOI":"10.1016\/j.preteyeres.2019.04.003","article-title":"Deep learning in ophthalmology: The technical and clinical considerations","volume":"72","author":"Ting","year":"2019","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tsiknakis, N., Theodoropoulos, D., Manikis, G., Ktistakis, E., Boutsora, O., Berto, A., Scarpa, F., Scarpa, A., Fotiadis, D.I., and Marias, K. (2021). Deep learning for diabetic retinopathy detection and classification based on fundus images: A review. Comput. Biol. Med., 135.","DOI":"10.1016\/j.compbiomed.2021.104599"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, J.-H., and Liu, T.Y.A. (2022). Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review. J. Clin. Med., 12.","DOI":"10.3390\/jcm12010152"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1007\/s10278-022-00640-9","article-title":"A Detailed Systematic Review on Retinal Image Segmentation Methods","volume":"35","author":"Panda","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Moradi, M., Chen, Y., Du, X., and Seddon, J.M. (2023). Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans. Comput. Biol. Med., 154.","DOI":"10.1016\/j.compbiomed.2022.106512"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s00417-017-3839-y","article-title":"OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications","volume":"256","author":"Prahs","year":"2017","journal-title":"Graefe\u2019s Arch. Clin. Exp. Ophthalmol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e69","DOI":"10.1016\/j.ophtha.2022.02.008","article-title":"Artificial Intelligence for Retinopathy of Prematurity","volume":"129","author":"Campbell","year":"2022","journal-title":"Ophthalmology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e2020016618","DOI":"10.1542\/peds.2020-016618","article-title":"Applications of Artificial Intelligence for Retinopathy of Prematurity Screening","volume":"147","author":"Campbell","year":"2021","journal-title":"Pediatrics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s10916-010-9454-7","article-title":"Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review","volume":"36","author":"Faust","year":"2010","journal-title":"J. Med. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"946915","DOI":"10.3389\/fendo.2022.946915","article-title":"Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy","volume":"13","author":"Huang","year":"2022","journal-title":"Front. Endocrinol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1016\/j.ophtha.2019.06.005","article-title":"A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs","volume":"126","author":"Keenan","year":"2019","journal-title":"Ophthalmology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s42256-020-0154-9","article-title":"Deep-learning-based prediction of late age-related macular degeneration progression","volume":"2","author":"Yan","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/978-3-031-21014-3_2","article-title":"Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning","volume":"13583","author":"Lee","year":"2022","journal-title":"Mach. Learn Med. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Morano, J., Hervella, S., Rouco, J., Novo, J., Fern\u00e1ndez-Vigo, J.I., and Ortega, M. (2023). Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning. Comput. Methods Programs Biomed., 229.","DOI":"10.1016\/j.cmpb.2022.107296"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e157968","DOI":"10.1172\/JCI157968","article-title":"A deep-learning system predicts glaucoma incidence and progression using retinal photographs","volume":"132","author":"Li","year":"2022","journal-title":"J. Clin. Investig."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.survophthal.2022.08.005","article-title":"Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review","volume":"68","author":"Coan","year":"2022","journal-title":"Surv. Ophthalmol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, L., Tang, L., Xia, M., and Cao, G. (2023). The application of artificial intelligence in glaucoma diagnosis and prediction. Front. Cell Dev. Biol., 11.","DOI":"10.3389\/fcell.2023.1173094"},{"key":"ref_34","first-page":"1","article-title":"Application of artificial intelligence in cataract management: Current and future directions","volume":"9","author":"Gutierrez","year":"2022","journal-title":"Eye Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s13139-019-00574-1","article-title":"Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning","volume":"53","author":"Park","year":"2019","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1167\/tvst.9.2.53","article-title":"Keratoconus Screening Based on Deep Learning Approach of Corneal Topography","volume":"9","author":"Kuo","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_37","first-page":"e16225","article-title":"Deep Learning\u2013Based Prediction of Refractive Error Using Photorefraction Images Captured by a Smartphone: Model Development and Validation Study","volume":"8","author":"Chun","year":"2020","journal-title":"JMIR Public Heal. Surveill."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e229960","DOI":"10.1001\/jamanetworkopen.2022.9960","article-title":"Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases","volume":"5","author":"Dong","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1056\/NEJMoa1917130","article-title":"Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs","volume":"382","author":"Milea","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1038\/s41598-023-28680-3","article-title":"Deep learning-based hemorrhage detection for diabetic retinopathy screening","volume":"13","author":"Aziz","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s40537-022-00632-0","article-title":"Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network","volume":"9","author":"Skouta","year":"2022","journal-title":"J. Big Data"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1111\/ceo.13670","article-title":"New grading criterion for retinal haemorrhages in term newborns based on deep convolutional neural networks","volume":"48","author":"Mao","year":"2020","journal-title":"Clin. Exp. Ophthalmol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6174","DOI":"10.1038\/s41598-022-09675-y","article-title":"State-of-the-art retinal vessel segmentation with minimalistic models","volume":"12","author":"Galdran","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1162\/neco.1997.9.2.349","article-title":"Statistical Inference, Occam\u2019s Razor, and Statistical Mechanics on the Space of Probability Distributions","volume":"9","author":"Balasubramanian","year":"1997","journal-title":"Neural Comput."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_46","unstructured":"Devries, T., and Taylor, G.W. (2017). Improved Regularization of Convolutional Neural Networks with Cutout. arXiv."},{"key":"ref_47","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 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., and Tang, X. (2017, January 21\u201326). Residual attention network for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_49","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_50","unstructured":"Vasu, P.K.A., Gabriel, J., Zhu, J., Tuzel, O., and Ranjan, A. (2023). FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization. arXiv."},{"key":"ref_51","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Proceedings of the NeurIPS, Vancouver, BC, Canada."},{"key":"ref_52","unstructured":"(2023, September 01). PyTorch Image Models, 2019, 10.5281\/zenodo. 4414861. GitHub Repository. Available online: https:\/\/github.com\/rwightman\/pytorch-image-models."},{"key":"ref_53","unstructured":"Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., and Beyer, L. (2021). How to train your vit? data, augmentation, and regularization in vision transformers. arXiv."},{"key":"ref_54","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_55","unstructured":"Bergstra, J., Yamins, D., and Cox, D.D. (2013, January 16\u201321). Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. Proceedings of the 30th International Conference on Machine Learning (ICML 2013), Atlanta, GA, USA."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/24\/20\/15105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:05:20Z","timestamp":1760130320000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/24\/20\/15105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,12]]},"references-count":55,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["ijms242015105"],"URL":"https:\/\/doi.org\/10.3390\/ijms242015105","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,12]]}}}