{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:29:21Z","timestamp":1790296161782,"version":"4.1.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int Ophthalmol"],"DOI":"10.1007\/s10792-025-03602-6","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T05:17:38Z","timestamp":1751001458000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Advanced glaucoma disease segmentation and classification with grey wolf optimized U\u2212Net++ and capsule networks"],"prefix":"10.1007","volume":"45","author":[{"given":"I.","family":"Govindharaj","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"W.","family":"Deva Priya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. L. S.","family":"Soujanya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. P.","family":"Senthilkumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Shantha Shalini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Ravichandran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"3602_CR1","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2017.080606","author":"Q Abbas","year":"2017","unstructured":"Abbas Q (2017) Glaucoma-deep: detection of glaucoma eye disease on retinal fundus images using deep learning. Int J Adv Comput Sci Appl. https:\/\/doi.org\/10.14569\/ijacsa.2017.080606","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"8","key":"3602_CR2","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/tpami.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828. https:\/\/doi.org\/10.1109\/tpami.2013.50","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"3602_CR3","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.3390\/sym14071427","volume":"14","author":"A Bilal","year":"2022","unstructured":"Bilal A, Zhu L, Deng A, Lu H, Wu N (2022) AI-Based automatic detection and classification of diabetic retinopathy using U-Net and deep learning. Symmetry 14(7):1427. https:\/\/doi.org\/10.3390\/sym14071427","journal-title":"Symmetry"},{"key":"3602_CR4","doi-asserted-by":"publisher","first-page":"107073","DOI":"10.1016\/j.cmpb.2022.107073","volume":"225","author":"W Chen","year":"2022","unstructured":"Chen W, Huang H, Huang J, Wang K, Qin H, Wong KKL (2022) Deep learning-based medical image segmentation of the aorta using XR-MSF-U-Net. Comput Methods Programs Biomed 225:107073. https:\/\/doi.org\/10.1016\/j.cmpb.2022.107073","journal-title":"Comput Methods Programs Biomed"},{"issue":"2","key":"3602_CR5","doi-asserted-by":"publisher","first-page":"15","DOI":"10.4018\/ijaec.2020040102","volume":"11","author":"LK Singh","year":"2020","unstructured":"Singh LK, Pooja N, Garg H (2020) Detection of glaucoma in retinal images based on multiobjective approach. Int J Appl Evol Comput 11(2):15\u201327. https:\/\/doi.org\/10.4018\/ijaec.2020040102","journal-title":"Int J Appl Evol Comput"},{"key":"3602_CR6","doi-asserted-by":"publisher","unstructured":"DS, Rajaram G, R E, J, V., I, G., & J, R. (2025). Enhanced Leukemia Prediction Using Hybrid Ant Colony And Ant Lion Optimization For Gene Selection And Classification. MethodsX, 103239. https:\/\/doi.org\/10.1016\/j.mex.2025.103239","DOI":"10.1016\/j.mex.2025.103239"},{"key":"3602_CR7","doi-asserted-by":"publisher","DOI":"10.19153\/cleiej.19.2.4","author":"M De Lima Claro","year":"2016","unstructured":"De Lima Claro M, De Moura Santos L, Silva WLE, De Ara\u00fajo FHD, De Moura NH, Santana AM (2016) Automatic glaucoma detection based on optic disc segmentation and texture feature extraction. CLEI Electron J. https:\/\/doi.org\/10.19153\/cleiej.19.2.4","journal-title":"CLEI Electron J"},{"issue":"12","key":"3602_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.9734\/bjmmr\/2016\/19617","volume":"11","author":"A Dey","year":"2015","unstructured":"Dey A, Bandyopadhyay S (2015) Automated glaucoma detection using Support vector machine classification method. Br J Med Med Res 11(12):1\u201312. https:\/\/doi.org\/10.9734\/bjmmr\/2016\/19617","journal-title":"Br J Med Med Res"},{"key":"3602_CR9","doi-asserted-by":"publisher","unstructured":"Singh LK, Garg H, Pooja N (2019). Automated glaucoma type identification using machine learning or deep learning techniques. In Algorithms for intelligent systems. pp. 241\u2013263. https:\/\/doi.org\/10.1007\/978-981-15-1100-4_12","DOI":"10.1007\/978-981-15-1100-4_12"},{"key":"3602_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-18630-0","author":"S Garg","year":"2024","unstructured":"Garg S, Sharma S, Dhariwal S, Priya WD, Singh M, Ramesh S (2024) Human crowd behaviour analysis based on video segmentation and classification using expectation\u2013maximization with deep learning architectures. Multimedia Tools Appl. https:\/\/doi.org\/10.1007\/s11042-024-18630-0","journal-title":"Multimedia Tools Appl"},{"issue":"7","key":"3602_CR11","doi-asserted-by":"publisher","first-page":"6734","DOI":"10.1007\/s11227-020-03566-7","volume":"77","author":"L Duan","year":"2021","unstructured":"Duan L, Yang S, Zhang D (2021) Multilevel thresholding using an improved cuckoo search algorithm for image segmentation. J Supercomput 77(7):6734\u20136753. https:\/\/doi.org\/10.1007\/s11227-020-03566-7","journal-title":"J Supercomput"},{"issue":"11","key":"3602_CR12","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1109\/tmi.2018.2837012","volume":"37","author":"H Fu","year":"2018","unstructured":"Fu H, Cheng J, Xu Y, Zhang C, Wong DWK, Liu J, Cao X (2018) Disc-Aware Ensemble Network for Glaucoma Screening from Fundus Image. IEEE Trans Med Imaging 37(11):2493\u20132501. https:\/\/doi.org\/10.1109\/tmi.2018.2837012","journal-title":"IEEE Trans Med Imaging"},{"issue":"24","key":"3602_CR13","doi-asserted-by":"publisher","first-page":"34737","DOI":"10.1007\/s11042-021-11559-8","volume":"81","author":"H Garg","year":"2022","unstructured":"Garg H, Gupta N, Agrawal R, Shivani S, Sharma B (2022) A real time cloud-based framework for glaucoma screening using EfficientNet. Multimedia Tools Appl 81(24):34737\u201334758. https:\/\/doi.org\/10.1007\/s11042-021-11559-8","journal-title":"Multimedia Tools Appl"},{"key":"3602_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s10792-024-03391-4","author":"I Govindharaj","year":"2025","unstructured":"Govindharaj I, Rampriya R, Michael G, Yazhinian S, Kumar KD, Anandh R (2025) Capsule network-based deep learning for early and accurate diabetic retinopathy detection. Int Ophthalmol. https:\/\/doi.org\/10.1007\/s10792-024-03391-4","journal-title":"Int Ophthalmol"},{"issue":"12","key":"3602_CR15","doi-asserted-by":"publisher","first-page":"5103","DOI":"10.3390\/app14125103","volume":"14","author":"H Almeshrky","year":"2024","unstructured":"Almeshrky H, Karac\u0131 A (2024) Optic disc segmentation in human retina images using a meta heuristic optimization method and disease diagnosis with deep learning. Appl Sci 14(12):5103. https:\/\/doi.org\/10.3390\/app14125103","journal-title":"Appl Sci"},{"key":"3602_CR16","doi-asserted-by":"publisher","unstructured":"I G. A P. Rajaram G. D S. S R. R V. P. K U. & S Y (2024) Enhanced diabetic retinopathy detection using U-Shaped network and Capsule Network-Driven Deep learning. MethodsX. 14:103052. https:\/\/doi.org\/10.1016\/j.mex.2024.103052","DOI":"10.1016\/j.mex.2024.103052"},{"key":"3602_CR17","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1109\/access.2021.3139160","volume":"10","author":"MT Islam","year":"2021","unstructured":"Islam MT, Mashfu ST, Faisal A, Siam SC, Naheen IT, Khan R (2021) Deep Learning-Based Glaucoma detection with cropped optic cup and disc and blood vessel segmentation. IEEE Access 10:2828\u20132841. https:\/\/doi.org\/10.1109\/access.2021.3139160","journal-title":"IEEE Access"},{"issue":"13","key":"3602_CR18","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.3390\/diagnostics13132274","volume":"13","author":"\u015e Kaba","year":"2023","unstructured":"Kaba \u015e, Haci H, Isin A, Ilhan A, Conkbayir C (2023) The application of deep learning for the segmentation and classification of coronary arteries. Diagnostics 13(13):2274. https:\/\/doi.org\/10.3390\/diagnostics13132274","journal-title":"Diagnostics"},{"key":"3602_CR19","doi-asserted-by":"publisher","first-page":"105069","DOI":"10.1016\/j.bspc.2023.105069","volume":"86","author":"VVNS Kumar","year":"2023","unstructured":"Kumar VVNS, Reddy GH, GiriPrasad M (2023) A novel glaucoma detection model using Unet++-based segmentation and ResNet with GRU-based optimized deep learning. Biomed Signal Process Control 86:105069. https:\/\/doi.org\/10.1016\/j.bspc.2023.105069","journal-title":"Biomed Signal Process Control"},{"key":"3602_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s42452-022-04984-3","author":"J Latif","year":"2022","unstructured":"Latif J, Tu S, Xiao C, Rehman SU, Imran A, Latif Y (2022) ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images. SN Appl Sci. https:\/\/doi.org\/10.1007\/s42452-022-04984-3","journal-title":"SN Appl Sci"},{"issue":"32","key":"3602_CR21","doi-asserted-by":"publisher","first-page":"77873","DOI":"10.1007\/s11042-024-18624-y","volume":"83","author":"LK Singh","year":"2024","unstructured":"Singh LK, Khanna M, Singh R (2024) Feature subset selection through nature inspired computing for efficient glaucoma classification from fundus images. Multimedia Tools Appl 83(32):77873\u201377944. https:\/\/doi.org\/10.1007\/s11042-024-18624-y","journal-title":"Multimedia Tools Appl"},{"issue":"2","key":"3602_CR22","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1109\/tmi.2019.2927226","volume":"39","author":"L Li","year":"2019","unstructured":"Li L, Xu M, Liu H, Li Y, Wang X, Jiang L, Wang Z, Fan X, Wang N (2019) A Large-Scale database and a CNN model for attention-based glaucoma detection. IEEE Trans Med Imaging 39(2):413\u2013424. https:\/\/doi.org\/10.1109\/tmi.2019.2927226","journal-title":"IEEE Trans Med Imaging"},{"key":"3602_CR23","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1109\/icip49359.2023.10222689","volume":"2022","author":"D Das","year":"2023","unstructured":"Das D, Nayak DR (2023) GS-NET: global self-attention guided CNN for Multi-stage glaucoma classification. IEEE Int Conf Image Process 2022:3454\u20133458. https:\/\/doi.org\/10.1109\/icip49359.2023.10222689","journal-title":"IEEE Int Conf Image Process"},{"key":"3602_CR24","doi-asserted-by":"publisher","unstructured":"I, G., T, R., A, P., P, S. K., P, U., & S, R. (2025c) Grey Wolf optimization technique with U-shaped and capsule networks: a novel framework for glaucoma diagnosis. MethodsX. https:\/\/doi.org\/10.1016\/j.mex.2025.103285","DOI":"10.1016\/j.mex.2025.103285"},{"issue":"14","key":"3602_CR25","doi-asserted-by":"publisher","first-page":"43223","DOI":"10.1007\/s11042-023-17044-8","volume":"83","author":"LK Singh","year":"2023","unstructured":"Singh LK, Khanna M, Singh R (2023) Efficient feature selection for breast cancer classification using soft computing approach: a novel clinical decision support system. Multimedia Tools Appl 83(14):43223\u201343276. https:\/\/doi.org\/10.1007\/s11042-023-17044-8","journal-title":"Multimedia Tools Appl"},{"issue":"02","key":"3602_CR26","doi-asserted-by":"publisher","first-page":"105","DOI":"10.25077\/jitce.4.02.105-109.2020","volume":"4","author":"PH Prastyo","year":"2020","unstructured":"Prastyo PH, Sumi AS, Nuraini A (2020) optic cup segmentation using u-net architecture on retinal fundus image. JITCE 4(02):105\u2013109. https:\/\/doi.org\/10.25077\/jitce.4.02.105-109.2020","journal-title":"JITCE"},{"key":"3602_CR27","doi-asserted-by":"publisher","first-page":"102004","DOI":"10.1016\/j.bspc.2020.102004","volume":"60","author":"J Pruthi","year":"2020","unstructured":"Pruthi J, Khanna K, Arora S (2020) Optic cup segmentation from retinal fundus images using glowworm swarm optimization for glaucoma detection. Biomed Signal Process Control 60:102004. https:\/\/doi.org\/10.1016\/j.bspc.2020.102004","journal-title":"Biomed Signal Process Control"},{"issue":"7","key":"3602_CR28","doi-asserted-by":"publisher","first-page":"3431","DOI":"10.3390\/s23073431","volume":"23","author":"V Raudonis","year":"2023","unstructured":"Raudonis V, Kairys A, Verkauskiene R, Sokolovska J, Petrovski G, Balciuniene VJ, Volke V (2023) Automatic detection of microaneurysms in FunDUs images using an Ensemble-Based segmentation method. Sensors 23(7):3431. https:\/\/doi.org\/10.3390\/s23073431","journal-title":"Sensors"},{"key":"3602_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2024.103116","volume":"14","author":"D Santhakumar","year":"2024","unstructured":"Santhakumar D, Pugazharasi K, Ravichandran S, Prabhu RV, Raja J (2024) Enhancing glaucoma diagnosis: generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2. MethodsX 14:103116. https:\/\/doi.org\/10.1016\/j.mex.2024.103116","journal-title":"MethodsX"},{"key":"3602_CR30","doi-asserted-by":"publisher","first-page":"101657","DOI":"10.1016\/j.compmedimag.2019.101657","volume":"78","author":"A Sarhan","year":"2019","unstructured":"Sarhan A, Rokne J, Alhajj R (2019) Glaucoma detection using image processing techniques: a literature review. Comput Med Imaging Graph 78:101657. https:\/\/doi.org\/10.1016\/j.compmedimag.2019.101657","journal-title":"Comput Med Imaging Graph"},{"key":"3602_CR31","doi-asserted-by":"publisher","DOI":"10.4108\/eetpht.9.4283","author":"L Kumar","year":"2023","unstructured":"Kumar L, Anitha C, Ghodke VN, Nithya N, Drave VA, Farhana A (2023) Deep learning based healthcare method for effective heart disease prediction. EAI Endorsed Trans Pervas Health Technol. https:\/\/doi.org\/10.4108\/eetpht.9.4283","journal-title":"EAI Endorsed Trans Pervas Health Technol"},{"issue":"4","key":"3602_CR32","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/tpami.2016.2572683","volume":"39","author":"E Shelhamer","year":"2016","unstructured":"Shelhamer E, Long J, Darrell T (2016) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640\u2013651. https:\/\/doi.org\/10.1109\/tpami.2016.2572683","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3602_CR33","doi-asserted-by":"publisher","unstructured":"Singh LK, Pooja N, Garg H, Khanna M, Bhadoria RS (2020) An analytical study on machine learning techniques. In: Advances in data mining and database management book series. pp. 137\u2013157. https:\/\/doi.org\/10.4018\/978-1-7998-5876-8.ch007","DOI":"10.4018\/978-1-7998-5876-8.ch007"},{"key":"3602_CR34","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/dasa54658.2022.9765303","volume":"2022","author":"T Shyamalee","year":"2022","unstructured":"Shyamalee T, Meedeniya D (2022) Attention U-Net for glaucoma identification using Fundus image segmentation. Int Conf Decis Aid Sci Appl 2022:6\u201310. https:\/\/doi.org\/10.1109\/dasa54658.2022.9765303","journal-title":"Int Conf Decis Aid Sci Appl"},{"key":"3602_CR35","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.patrec.2021.10.030","volume":"155","author":"C Anitha","year":"2021","unstructured":"Anitha C, Kumar RS (2021) GEVE: A generative adversarial network for extremely dark image\/video enhancement. Pattern Recogn Lett 155:159\u2013164. https:\/\/doi.org\/10.1016\/j.patrec.2021.10.030","journal-title":"Pattern Recogn Lett"},{"issue":"22","key":"3602_CR36","doi-asserted-by":"publisher","first-page":"16129","DOI":"10.1007\/s00521-021-06554-x","volume":"35","author":"G Sun","year":"2021","unstructured":"Sun G, Zhang Z, Zhang J, Zhu M, Zhu X, Yang J, Li Y (2021) Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening. Neural Comput Appl 35(22):16129\u201316142. https:\/\/doi.org\/10.1007\/s00521-021-06554-x","journal-title":"Neural Comput Appl"},{"key":"3602_CR37","doi-asserted-by":"publisher","unstructured":"Wang Z, Xie Z, Xu Y (2021). Automatic segmentation for retinal vessel using concatenate UNET++. In: Lecture notes on data engineering and communications technologies. pp. 10\u201318. https:\/\/doi.org\/10.1007\/978-3-030-89508-2_2","DOI":"10.1007\/978-3-030-89508-2_2"},{"key":"3602_CR38","doi-asserted-by":"publisher","unstructured":"ChenX, Xu Y, Kee Wong W, Wong TY, Liu J (2015) Glaucoma detection based on deep convolutional neural network. In: 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), Milan, Italy, 2015, pp. 715\u2013718. https:\/\/doi.org\/10.1109\/embc.2015.7318462","DOI":"10.1109\/embc.2015.7318462"},{"issue":"26","key":"3602_CR39","doi-asserted-by":"publisher","first-page":"41207","DOI":"10.1007\/s11042-023-15174-7","volume":"82","author":"Y Xu","year":"2023","unstructured":"Xu Y, Meng F, Yang H, Lu S, Wang H, Hu M (2023) E-Net: a novel deep learning framework integrating expert knowledge for glaucoma optic disc hemorrhage segmentation. Multimedia Tools Appl 82(26):41207\u201341224. https:\/\/doi.org\/10.1007\/s11042-023-15174-7","journal-title":"Multimedia Tools Appl"},{"key":"3602_CR40","doi-asserted-by":"publisher","first-page":"104308","DOI":"10.1016\/j.dsp.2023.104308","volume":"145","author":"S Yan","year":"2023","unstructured":"Yan S, Pan X, Wang Y (2023) MRSNet: Joint consistent optic disc and cup segmentation based on large kernel residual convolutional attention and self-attention. Digital Signal Process 145:104308. https:\/\/doi.org\/10.1016\/j.dsp.2023.104308","journal-title":"Digital Signal Process"},{"key":"3602_CR41","doi-asserted-by":"publisher","first-page":"35778","DOI":"10.1109\/access.2021.3061690","volume":"9","author":"G Zhang","year":"2021","unstructured":"Zhang G, Pan J, Zhang Z, Zhang H, Xing C, Sun B, Li M (2021) Hybrid graph convolutional network for semi-supervised retinal image classification. IEEE Access 9:35778\u201335789. https:\/\/doi.org\/10.1109\/access.2021.3061690","journal-title":"IEEE Access"}],"container-title":["International Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10792-025-03602-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10792-025-03602-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10792-025-03602-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T18:06:19Z","timestamp":1766167579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10792-025-03602-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,27]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3602"],"URL":"https:\/\/doi.org\/10.1007\/s10792-025-03602-6","relation":{},"ISSN":["1573-2630"],"issn-type":[{"value":"1573-2630","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,27]]},"assertion":[{"value":"8 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 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":"Conflict of interest"}},{"value":"In this Manuscript no, human participants or animals their data or biological material, are not involved.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"266"}}