{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T20:49:57Z","timestamp":1790628597240,"version":"4.1.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-15712-3","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T15:05:04Z","timestamp":1755529504000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep learning for retinal non-perfusion and foveal avascular zone analysis in wide-field OCTA in diabetic retinopathy"],"prefix":"10.1038","volume":"15","author":[{"given":"Hugo","family":"Le Boit\u00e9","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Bonnin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathias","family":"Gallardo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathieu","family":"Lamard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aude","family":"Couturier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gwenol\u00e9","family":"Quellec","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"15712_CR1","unstructured":"World Health Organization. Global report on diabetes [Internet]. Geneva: World Health Organization. [cited 2022 May 17]. 83 p. (2016). Available from: https:\/\/apps.who.int\/iris\/handle\/10665\/204871"},{"key":"15712_CR2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.visres.2017.04.003","volume":"139","author":"J Lechner","year":"2017","unstructured":"Lechner, J., O\u2019Leary, O. E. & Stitt, A. W. The pathology associated with diabetic retinopathy. Vision. Res. 139, 7\u201314 (2017).","journal-title":"Vision. Res."},{"key":"15712_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/3801570","volume":"2016","author":"VJ Vieira-Potter","year":"2016","unstructured":"Vieira-Potter, V. J., Karamichos, D. & Lee, D. J. Ocular complications of diabetes and therapeutic approaches. Biomed. Res. Int. 2016, 1\u201314 (2016).","journal-title":"Biomed. Res. Int."},{"issue":"1","key":"15712_CR4","first-page":"218","volume":"27","author":"Y Yamana","year":"1983","unstructured":"Yamana, Y., Ohnishi, Y., Taniguchi, Y. & Ikeda, M. Early signs of diabetic retinopathy by fluorescein angiography. Jpn J. Ophthalmol. 27 (1), 218\u2013227 (1983).","journal-title":"Jpn J. Ophthalmol."},{"key":"15712_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.preteyeres.2017.11.003","volume":"64","author":"RF Spaide","year":"2018","unstructured":"Spaide, R. F., Fujimoto, J. G., Waheed, N. K., Sadda, S. R. & Staurenghi, G. Optical coherence tomography angiography. Prog. Retin. Eye Res. 64, 1\u201355 (2018).","journal-title":"Prog. Retin. Eye Res."},{"issue":"6","key":"15712_CR6","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1001\/jamaophthalmol.2016.0600","volume":"134","author":"DA Salz","year":"2016","unstructured":"Salz, D. A. et al. Select features of diabetic retinopathy on swept-source optical coherence tomographic angiography compared with fluorescein angiography and normal eyes. JAMA Ophthalmol. 134 (6), 644 (2016).","journal-title":"JAMA Ophthalmol."},{"issue":"7","key":"15712_CR7","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1007\/s00417-018-3992-y","volume":"256","author":"O Sawada","year":"2018","unstructured":"Sawada, O. et al. Comparison between wide-angle OCT angiography and ultra-wide field fluorescein angiography for detecting non-perfusion areas and retinal neovascularization in eyes with diabetic retinopathy. Graefes Arch. Clin. Exp. Ophthalmol. 256 (7), 1275\u20131280 (2018).","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"issue":"9","key":"15712_CR8","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1136\/bjophthalmol-2017-311358","volume":"102","author":"T Hirano","year":"2018","unstructured":"Hirano, T. et al. Wide-field En face swept-source optical coherence tomography angiography using extended field imaging in diabetic retinopathy. Br. J. Ophthalmol. 102 (9), 1199\u20131203 (2018).","journal-title":"Br. J. Ophthalmol."},{"issue":"1","key":"15712_CR9","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.ajo.2015.04.021","volume":"160","author":"A Ishibazawa","year":"2015","unstructured":"Ishibazawa, A. et al. Optical coherence tomography angiography in diabetic retinopathy: A prospective pilot study. Am. J. Ophthalmol. 160 (1), 35\u201344e1 (2015).","journal-title":"Am. J. Ophthalmol."},{"issue":"12","key":"15712_CR10","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1016\/j.ophtha.2019.06.022","volume":"126","author":"A Couturier","year":"2019","unstructured":"Couturier, A. et al. Widefield OCT-Angiography and fluorescein angiography assessments of nonperfusion in diabetic retinopathy and edema treated with anti\u2013vascular endothelial growth factor. Ophthalmology 126 (12), 1685\u20131694 (2019).","journal-title":"Ophthalmology"},{"issue":"4","key":"15712_CR11","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1001\/jamaophthalmol.2015.5658","volume":"134","author":"TS Hwang","year":"2016","unstructured":"Hwang, T. S. et al. Automated quantification of capillary nonperfusion using optical coherence tomography angiography in diabetic retinopathy. JAMA Ophthalmol. 134 (4), 367 (2016).","journal-title":"JAMA Ophthalmol."},{"issue":"11","key":"15712_CR12","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1097\/IAE.0000000000000862","volume":"35","author":"SA Agemy","year":"2015","unstructured":"Agemy, S. A. et al. Retinal vascular perfusion density mapping using optical coherence tomography angiography in normals and diabetic retinopathy patients. Retina 35 (11), 2353\u20132363 (2015).","journal-title":"Retina"},{"issue":"8","key":"15712_CR13","doi-asserted-by":"publisher","first-page":"3907","DOI":"10.1167\/iovs.16-19570","volume":"57","author":"M Al-Sheikh","year":"2016","unstructured":"Al-Sheikh, M., Akil, H., Pfau, M., Sadda, S. R. & Swept-Source, O. C. T. Angiography imaging of the foveal avascular zone and macular capillary network density in diabetic retinopathy. Invest. Ophthalmol. Vis. Sci. 57 (8), 3907 (2016).","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"6","key":"15712_CR14","doi-asserted-by":"publisher","first-page":"BIO307","DOI":"10.1167\/iovs.17-21787","volume":"58","author":"PL Nesper","year":"2017","unstructured":"Nesper, P. L. et al. Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography. Investig. Ophthalmol. Vis. Sci. 58 (6), BIO307\u2013BIO315 (2017).","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"issue":"7","key":"15712_CR15","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1001\/jamaophthalmol.2018.1319","volume":"136","author":"B Dupas","year":"2018","unstructured":"Dupas, B. et al. Association between vessel density and visual acuity in patients with diabetic retinopathy and poorly controlled type 1 diabetes. JAMA Ophthalmol. 136 (7), 721 (2018).","journal-title":"JAMA Ophthalmol."},{"issue":"Supplement 1","key":"15712_CR16","doi-asserted-by":"publisher","first-page":"S93","DOI":"10.1097\/IAE.0000000000001288","volume":"36","author":"J Schottenhamml","year":"2016","unstructured":"Schottenhamml, J. et al. An automatic, intercapillary area-based algorithm for quantifying diabetes-related capillary dropout using optical coherence tomography angiography. Retina 36 (Supplement 1), S93\u2013101 (2016).","journal-title":"Retina"},{"key":"15712_CR17","doi-asserted-by":"crossref","unstructured":"Fayed, A. E., Abdelbaki, A. M., El Zawahry, O. M. & Fawzi, A. A. Optical coherence tomography angiography reveals progressive worsening of retinal vascular geometry in diabetic retinopathy and improved geometry after panretinal photocoagulation. Vavvas DG, editor. PLoS One. 14 (12), e0226629 (2019).","DOI":"10.1371\/journal.pone.0226629"},{"issue":"1","key":"15712_CR18","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1001\/jamaophthalmol.2018.4982","volume":"137","author":"LP Aiello","year":"2019","unstructured":"Aiello, L. P. et al. Comparison of early treatment diabetic retinopathy study standard 7-Field imaging with Ultrawide-Field imaging for determining severity of diabetic retinopathy. JAMA Ophthalmol. 137 (1), 65\u201373 (2019).","journal-title":"JAMA Ophthalmol."},{"issue":"9","key":"15712_CR19","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1167\/tvst.12.9.15","volume":"12","author":"H Le Boit\u00e9","year":"2023","unstructured":"Le Boit\u00e9, H., Gaudric, A., Erginay, A., Tadayoni, R. & Couturier, A. Is there a nonperfusion threshold on OCT angiography associated with new vessels detected on Ultra-Wide-Field imaging in diabetic retinopathy?? Transl Vis. Sci. Technol. 12 (9), 15 (2023).","journal-title":"Transl Vis. Sci. Technol."},{"issue":"18","key":"15712_CR20","doi-asserted-by":"publisher","first-page":"E2395","DOI":"10.1073\/pnas.1500185112","volume":"112","author":"Y Jia","year":"2015","unstructured":"Jia, Y. et al. Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye. Proc. Natl. Acad. Sci. USA. 112 (18), E2395\u2013E2402 (2015).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"issue":"3","key":"15712_CR21","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1097\/IAE.0000000000002403","volume":"40","author":"AY Alibhai","year":"2020","unstructured":"Alibhai, A. Y. et al. Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography. Retina 40 (3), 412\u2013420 (2020).","journal-title":"Retina"},{"issue":"2","key":"15712_CR22","doi-asserted-by":"publisher","first-page":"100144","DOI":"10.1016\/j.xops.2022.100144","volume":"2","author":"I Garg","year":"2022","unstructured":"Garg, I. et al. Nonperfusion area and other vascular metrics by wider field Swept-Source OCT angiography as biomarkers of diabetic retinopathy severity. Ophthalmol. Sci. 2 (2), 100144 (2022).","journal-title":"Ophthalmol. Sci."},{"issue":"1","key":"15712_CR23","doi-asserted-by":"publisher","first-page":"9096","DOI":"10.1038\/s41598-019-43958-1","volume":"9","author":"LR De Pretto","year":"2019","unstructured":"De Pretto, L. R. et al. Controlling for artifacts in widefield optical coherence tomography angiography measurements of Non-Perfusion area. Sci. Rep. 9 (1), 9096 (2019).","journal-title":"Sci. Rep."},{"issue":"1","key":"15712_CR24","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.oret.2017.05.007","volume":"2","author":"A Rabiolo","year":"2018","unstructured":"Rabiolo, A. et al. Correlation analysis between foveal avascular zone and peripheral ischemic index in diabetic retinopathy: A pilot study. Ophthalmol. Retina. 2 (1), 46\u201352 (2018).","journal-title":"Ophthalmol. Retina"},{"issue":"4","key":"15712_CR25","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s00417-019-04264-8","volume":"257","author":"M Niestrata-Ortiz","year":"2019","unstructured":"Niestrata-Ortiz, M., Fichna, P., Stankiewicz, W. & Stopa, M. Enlargement of the foveal avascular zone detected by optical coherence tomography angiography in diabetic children without diabetic retinopathy. Graefes Arch. Clin. Exp. Ophthalmol. 257 (4), 689\u2013697 (2019).","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"issue":"3","key":"15712_CR26","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1038\/sj.eye.6701456","volume":"19","author":"J Conrath","year":"2005","unstructured":"Conrath, J., Giorgi, R., Raccah, D. & Ridings, B. Foveal avascular zone in diabetic retinopathy: Quantitative vs qualitative assessment. Eye 19 (3), 322\u2013326 (2005).","journal-title":"Eye"},{"key":"15712_CR27","unstructured":"Lecun, Y. & Bengio, Y. Convolutional Networks for Images, Speech, and Time-Series. The Handbook of Brain Theory and Neural Networks. (1995)."},{"issue":"20","key":"15712_CR28","doi-asserted-by":"publisher","first-page":"2470","DOI":"10.3390\/electronics10202470","volume":"10","author":"D Bhatt","year":"2021","unstructured":"Bhatt, D. et al. CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics 10 (20), 2470 (2021).","journal-title":"Electronics"},{"key":"#cr-split#-15712_CR29.1","doi-asserted-by":"crossref","unstructured":"Zarandy, A., Rekeczky, C., Szolgay, P. & Chua, L. O. Overview of CNN research: 25 years history and the current trends. In: 2015 IEEE International Symposium on Circuits and Systems (ISCAS) [Internet]. Lisbon, Portugal: IEEE","DOI":"10.1109\/ISCAS.2015.7168655"},{"key":"#cr-split#-15712_CR29.2","unstructured":"[cited 2024 May 22]. pp. 401-4. (2015). Available from: http:\/\/ieeexplore.ieee.org\/document\/7168655\/"},{"issue":"4","key":"15712_CR30","doi-asserted-by":"publisher","first-page":"117","DOI":"10.3390\/bdcc6040117","volume":"6","author":"J Moorthy","year":"2022","unstructured":"Moorthy, J. & Gandhi, U. D. A survey on medical image segmentation based on deep learning techniques. Big Data Cogn. Comput. 6 (4), 117 (2022).","journal-title":"Big Data Cogn. Comput."},{"key":"15712_CR31","unstructured":"Kayalibay, B., Jensen, G. & van der Smagt, P. CNN-based segmentation of medical imaging data [Internet]. arXiv; 2017 [cited 2024 May 22]. Available from: http:\/\/arxiv.org\/abs\/1701.03056"},{"key":"15712_CR32","unstructured":"Suzuki, K. Survey of deep learning applications to medical. Image Anal.;35(4). (2017)."},{"issue":"8","key":"15712_CR33","first-page":"OD87","volume":"64","author":"H Le Boit\u00e9","year":"2023","unstructured":"Le Boit\u00e9, H. et al. Deep learning segmentation of retinal non-perfusion on OCTA. Investig. Ophthalmol. Vis. Sci. 64 (8), OD87 (2023).","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"15712_CR34","unstructured":"Qian, B. et al. DRAC: Diabetic retinopathy analysis challenge with ultra-wide optical coherence tomography angiography images [Internet]. arXiv; [cited 2024 May 22]. (2023). Available from: http:\/\/arxiv.org\/abs\/2304.02389"},{"issue":"4","key":"15712_CR35","doi-asserted-by":"publisher","first-page":"e084574","DOI":"10.1136\/bmjopen-2024-084574","volume":"14","author":"R Tadayoni","year":"2024","unstructured":"Tadayoni, R. et al. Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): Protocol for a prospective cohort. BMJ Open. 14 (4), e084574 (2024).","journal-title":"BMJ Open."},{"issue":"3","key":"15712_CR36","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31 (3), 1116\u20131128 (2006).","journal-title":"Neuroimage"},{"issue":"11","key":"15712_CR37","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1097\/IAE.0000000000000765","volume":"35","author":"RF Spaide","year":"2015","unstructured":"Spaide, R. F., Fujimoto, J. G. & Waheed, N. K. Image artifacts in optical coherence angiography. Retina 35 (11), 2163\u20132180 (2015).","journal-title":"Retina"},{"issue":"10","key":"15712_CR38","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1016\/j.oret.2020.12.022","volume":"5","author":"MR Munk","year":"2021","unstructured":"Munk, M. R. et al. Standardization of OCT angiography nomenclature in retinal vascular diseases: First survey results. Ophthalmol. Retina. 5 (10), 981\u2013990 (2021).","journal-title":"Ophthalmol. Retina"},{"issue":"7","key":"15712_CR39","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"Warfield, S. K., Zou, K. H. & Wells, W. M. Simultaneous truth and performance level Estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging. 23 (7), 903\u2013921 (2004).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"15712_CR40","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T. & U-Net convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention \u2013 MICCAI 2015 [Internet]. Cham: Springer International Publishing; [cited 2021 Nov 4]. pp. 234\u201341. (Lecture Notes in Computer Science; vol. 9351). Available from: http:\/\/link.springer.com\/ (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"15712_CR41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 [cited 2025 Feb 17]. pp. 770\u20138. Available from: https:\/\/openaccess.thecvf.com\/content_cvpr_2016\/html\/He_Deep_Residual_Learning_CVPR_2016_paper.html","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"15712_CR42","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev, A. et al. Albumentations: Fast and flexible image augmentations. Information 11 (2), 125 (2020).","journal-title":"Information"},{"key":"15712_CR43","unstructured":"Smith, L. N., Topin, N. & Super-Convergence, X. [cited 2024 May 22]. (2018). Available from: http:\/\/arxiv.org\/abs\/1708.07120"},{"issue":"7","key":"15712_CR44","first-page":"2057","volume":"63","author":"W Lewis","year":"2022","unstructured":"Lewis, W. et al. Comparison of procedural and neural network algorithms for segmentation of regions of non-perfusion in retinal OCTA scans. Investig. Ophthalmol. Vis. Sci. 63 (7), 2057\u2013F0046 (2022).","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"15712_CR45","unstructured":"Lin, L. et al. BSDA-Net: A boundary shape and distance aware joint learning framework for segmenting and classifying OCTA images [Internet]. arXiv; [cited 2024 May 22]. (2021). Available from: http:\/\/arxiv.org\/abs\/2107.04823"},{"issue":"1","key":"15712_CR46","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1038\/s41598-020-80058-x","volume":"11","author":"R Mirshahi","year":"2021","unstructured":"Mirshahi, R. et al. Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach. Sci. Rep. 11 (1), 1031 (2021).","journal-title":"Sci. Rep."},{"issue":"2","key":"15712_CR47","doi-asserted-by":"publisher","first-page":"100027","DOI":"10.1016\/j.xops.2021.100027","volume":"1","author":"Y Guo","year":"2021","unstructured":"Guo, Y. et al. Quantification of nonperfusion area in montaged widefield OCT angiography using deep learning in diabetic retinopathy. Ophthalmol. Sci. 1 (2), 100027 (2021).","journal-title":"Ophthalmol. Sci."},{"issue":"9","key":"15712_CR48","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1016\/j.oret.2022.02.007","volume":"6","author":"MR Munk","year":"2022","unstructured":"Munk, M. R. et al. Recommendations for OCT angiography reporting in retinal vascular disease: A Delphi approach by international experts. Ophthalmol. Retina. 6 (9), 753\u2013761 (2022).","journal-title":"Ophthalmol. Retina"},{"issue":"10","key":"15712_CR49","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1080\/17434440.2020.1831384","volume":"17","author":"A Wyl\u0119ga\u0142a","year":"2020","unstructured":"Wyl\u0119ga\u0142a, A., Bolek, B. & Wyl\u0119ga\u0142a, E. Trends in optical coherence tomography angiography use in university clinic and private practice setting between 2014\u20132018. Expert Rev. Med. Dev. 17 (10), 1109\u20131113 (2020).","journal-title":"Expert Rev. Med. Dev."},{"issue":"1","key":"15712_CR50","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1038\/s41377-022-00740-9","volume":"11","author":"DM Sampson","year":"2022","unstructured":"Sampson, D. M., Dubis, A. M., Chen, F. K., Zawadzki, R. J. & Sampson, D. D. Towards standardizing retinal optical coherence tomography angiography: A review. Light Sci. Appl. 11 (1), 63 (2022).","journal-title":"Light Sci. Appl."},{"key":"15712_CR51","unstructured":"Lu, D. et al. Retinal fluid segmentation and detection in optical coherence tomography images using fully convolutional neural network [Internet]. arXiv; 2017 [cited 2025 Feb 17]. Available from: http:\/\/arxiv.org\/abs\/1710.04778"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-15712-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-15712-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-15712-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T05:21:36Z","timestamp":1757395296000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-15712-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,18]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["15712"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-15712-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,18]]},"assertion":[{"value":"5 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"30225"}}