{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T15:59:48Z","timestamp":1784390388853,"version":"3.55.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100004917","name":"Cancer Prevention and Research Institute of Texas","doi-asserted-by":"publisher","award":["RP170668"],"award-info":[{"award-number":["RP170668"]}],"id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet\u2009+\u2009LSTM, GWO-InceptionV3 and GWO-VGG16\u2009+\u2009InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or\u00a0above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16\u2009+\u2009InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16\u2009+\u2009InceptionV3 models using SPECT DaTscan dataset.<\/jats:p>","DOI":"10.1186\/s12880-024-01335-z","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T01:01:21Z","timestamp":1719190881000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["An improved method for diagnosis of Parkinson\u2019s disease using deep learning models enhanced with metaheuristic algorithm"],"prefix":"10.1186","volume":"24","author":[{"given":"Babita","family":"Majhi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aarti","family":"Kashyap","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siddhartha Suprasad","family":"Mohanty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sujata","family":"Dash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saurav","family":"Mallik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aimin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongming","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"key":"1335_CR1","unstructured":"Michael J. For Foundation for Parkinson Research, Parkinson\u2019s disease causes, (Retrieved from https:\/\/www.michaeljfox.org\/understanding-parkinsons\/living-with-pd.html), 12 April 2023."},{"key":"1335_CR2","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.compbiomed.2018.09.008","volume":"102","author":"S Bhat","year":"2018","unstructured":"Bhat S, Acharya UR, Hagiwara Y, Dadmehr N, Adeli H. Parkinson\u2019s disease: cause factors, measurable indicators, and early diagnosis. Comput Biol Med. 2018;102:234\u201341.","journal-title":"Comput Biol Med"},{"key":"1335_CR3","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.future.2018.02.009","volume":"83","author":"E Abdulhay","year":"2018","unstructured":"Abdulhay E, Arunkumar N, Kumaravelu N, Vellaiappan E, Venkatraman V. Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Futur Gener Comput Syst. 2018;83:366\u201373.","journal-title":"Futur Gener Comput Syst"},{"issue":"2","key":"1335_CR4","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","volume":"29","author":"AS Lundervold","year":"2019","unstructured":"Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29(2):102\u201327.","journal-title":"Z Med Phys"},{"key":"1335_CR5","doi-asserted-by":"publisher","first-page":"165760","DOI":"10.1016\/j.ijleo.2020.165760","volume":"224","author":"UK Acharya","year":"2020","unstructured":"Acharya UK, Kumar S. Particle swarm optimized texture based histogram equalization (PSOTHE) for MRI brain image enhancement. Optik, Science Direct. 2020;224:165760. https:\/\/doi.org\/10.1016\/j.ijleo.2020.165760.","journal-title":"Optik, Science Direct"},{"key":"1335_CR6","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014;69:46\u201361. Elsevier.","journal-title":"Adv Eng Softw"},{"issue":"4","key":"1335_CR7","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE Comput Intell Magaz. 2006;1(4):28\u201339.","journal-title":"IEEE Comput Intell Magaz"},{"key":"1335_CR8","doi-asserted-by":"publisher","first-page":"119672","DOI":"10.1016\/j.eswa.2023.119672","volume":"219","author":"F Zhao","year":"2023","unstructured":"Zhao F, Wang Z, Wang L, Xu T, Zhu N. A multi-agent reinforcement learning driven artificial bee colony algorithm with the central controller. Expert Syst Appl. 2023;219:119672. https:\/\/doi.org\/10.1016\/j.eswa.2023.119672. Elsevier.","journal-title":"Expert Syst Appl"},{"issue":"2","key":"1335_CR9","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1049\/smt2.12023","volume":"15","author":"MS Shaikh","year":"2021","unstructured":"Shaikh MS, Hua C, Jatoi MA, Ansari MM, Qader AA. Application of grey wolf optimization algorithm in parameter calculation of overhead transmission line system. IET Sci Meas Technol. 2021;15(2):218\u201331.","journal-title":"IET Sci Meas Technol"},{"key":"1335_CR10","doi-asserted-by":"publisher","first-page":"104041","DOI":"10.1016\/j.compbiomed.2020.104041","volume":"126","author":"P Magesh","year":"2020","unstructured":"Magesh P, Myloth R, Tom R. An explainable machine learning model for early detection of Parkinson\u2019s disease using LIME on DaTscan imagery. Comput Biol Med. 2020;126:104041. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104041.","journal-title":"Comput Biol Med"},{"issue":"14","key":"1335_CR11","doi-asserted-by":"publisher","first-page":"908143","DOI":"10.3389\/fnagi.2022.908143","volume":"13","author":"M Thakur","year":"2022","unstructured":"Thakur M, Kuresan H, Dhanalakshmi S, Lai KW, Wu X. Soft attention based DenseNet model for Parkinson\u2019s disease classification using SPECT images. Front Aging Neurosci. 2022;13(14):908143. https:\/\/doi.org\/10.3389\/fnagi.2022.908143.","journal-title":"Front Aging Neurosci"},{"key":"1335_CR12","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.3390\/diagnostics12051173","volume":"12","author":"S Kurmi","year":"2022","unstructured":"Kurmi S, Shreya S, Sen A, Sinitca D, Sarkar R. An ensemble of CNN models for Parkinson\u2019s disease detection using DaTscan images. Diagnostics. 2022;12:1173. https:\/\/doi.org\/10.3390\/diagnostics12051173.","journal-title":"Diagnostics"},{"key":"1335_CR13","doi-asserted-by":"publisher","unstructured":"Basnin N, Nahar N, Anika FA, Hossain MS, Andersson K. Deep learning approach to classify Parkinson\u2019s disease from MRI samples, Brain Informatics. 2021:12960. https:\/\/doi.org\/10.1007\/978-3-030-86993-9_48. Springer.","DOI":"10.1007\/978-3-030-86993-9_48"},{"key":"1335_CR14","doi-asserted-by":"publisher","first-page":"103405","DOI":"10.1016\/j.nicl.2023.103405","volume":"38","author":"M Camacho","year":"2023","unstructured":"Camacho M, et al. Explainable classification of Parkinson\u2019s disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets. NeuroImage Clin. 2023;38:103405. https:\/\/doi.org\/10.1016\/j.nicl.2023.103405.","journal-title":"NeuroImage Clin."},{"key":"1335_CR15","doi-asserted-by":"publisher","first-page":"103315","DOI":"10.1016\/j.nicl.2023.103315","volume":"37","author":"H Baagil","year":"2018","unstructured":"Baagil H. Neural correlates of impulse control behaviors in Parkinson\u2019s disease: Analysis of multimodal imaging data. Neuroimage Clin. 2018;37:103315. https:\/\/doi.org\/10.1016\/j.nicl.2023.103315.","journal-title":"Neuroimage Clin"},{"key":"1335_CR16","doi-asserted-by":"publisher","first-page":"105793","DOI":"10.1016\/j.cmpb.2020.105793","volume":"198","author":"G Solana-Lavalle","year":"2021","unstructured":"Solana-Lavalle G, Rosas-Romero R. Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson\u2019s disease. Comput Methods Programs Biomed. 2021;198:105793. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105793.","journal-title":"Comput Methods Programs Biomed"},{"issue":"12","key":"1335_CR17","doi-asserted-by":"publisher","first-page":"648548","DOI":"10.3389\/fneur.2021.648548","volume":"14","author":"AS Talai","year":"2021","unstructured":"Talai AS, Sedlacik J, Boelmans K, Forckert ND. Utility of multi-modal MRI for differentiating of Parkinson\u2019s disease and progressive supranuclear palsy using machine learning. Front Neurol. 2021;14(12):648548. https:\/\/doi.org\/10.3389\/fneur.2021.648548.","journal-title":"Front Neurol."},{"issue":"6","key":"1335_CR18","doi-asserted-by":"publisher","first-page":"402","DOI":"10.3390\/diagnostics10060402","volume":"10","author":"S Chakraborty","year":"2020","unstructured":"Chakraborty S, Aich S, Kim HC. Detection of Parkinson\u2019s disease from 3T T1 weighted MRI scans using 3D convolutional neural network. Diagnostics (Basel). 2020;10(6):402.","journal-title":"Diagnostics (Basel)."},{"key":"1335_CR19","doi-asserted-by":"publisher","unstructured":"Wingate J, Kollia I, Bidaut L, Kollias S. A unified deep learning approach for prediction of Parkinson\u2019s disease. arXiv e. 2019. https:\/\/doi.org\/10.48550\/arXiv.1911.10653.","DOI":"10.48550\/arXiv.1911.10653"},{"key":"1335_CR20","first-page":"987","volume-title":"IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)","author":"TA Mostafa","year":"2020","unstructured":"Mostafa TA, Cheng I. Parkinson\u2019s Disease Detection Using Ensemble Architecture from MR Images *. In: IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). 2020. p. 987\u201392."},{"key":"1335_CR21","doi-asserted-by":"publisher","first-page":"15467","DOI":"10.1007\/s11042-019-7469-8","volume":"79","author":"S Sivaranjini","year":"2020","unstructured":"Sivaranjini S, Sujatha C. Deep learning based diagnosis of Parkinson\u2019s disease using convolutional neural network. Multimedia Tools and Applications. 2020;79:15467\u201379. https:\/\/doi.org\/10.1007\/s11042-019-7469-8.","journal-title":"Multimedia Tools and Applications"},{"key":"1335_CR22","doi-asserted-by":"publisher","unstructured":"Esmaeilzadeh S, Yao Y, Adeli E. End-to-end Parkinson disease diagnosis using brain MR-images by 3D-CNN. arXiv. 2018;1\u20137. https:\/\/doi.org\/10.48550\/arXiv.1806.05233.","DOI":"10.48550\/arXiv.1806.05233"},{"key":"1335_CR23","doi-asserted-by":"publisher","unstructured":"Shah PM, Zeb A, Shafi U, Zaidi SFA, Shah MA. Detection of Parkinson's disease in brain MRI using convolutional neural network. 2018 24th International Conference on Automation and Computing (ICAC), Newcastle Upon Tyne, UK; 2018. p.1\u20136. https:\/\/doi.org\/10.23919\/IConAC.2018.8749023.","DOI":"10.23919\/IConAC.2018.8749023"},{"key":"1335_CR24","doi-asserted-by":"publisher","unstructured":"Mei J, Tremblay C, Stikov N, Desrosiers C, Frasnelli J. Differentiation of Parkinson\u2019s disease and non-parkinsonian olfactory dysfunction with structural MRI data. Computer-Aided Diagnosis. International Society for Optics and Photonics; 2021. p.11597. 115971E. https:\/\/doi.org\/10.1117\/12.2581233.","DOI":"10.1117\/12.2581233"},{"issue":"4","key":"1335_CR25","first-page":"12","volume":"21","author":"R Pugalenthi","year":"2019","unstructured":"Pugalenthi R, Rajakumar RM, Ramya J, Rajinikanth V. Evaluation and classification of the brain tumor MRI using machine learning technique. J Control Eng Appl Inform. 2019;21(4):12\u201321.","journal-title":"J Control Eng Appl Inform."},{"key":"1335_CR26","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s13550-021-00795-6","volume":"11","author":"KH Leung","year":"2021","unstructured":"Leung KH, Rowe SP, Pomper MG. A three-stage, deep learning, ensemble approach for prognosis in patients with Parkinson\u2019s disease. EJNMMI Res. 2021;11:52. https:\/\/doi.org\/10.1186\/s13550-021-00795-6. SpringerOpen.","journal-title":"EJNMMI Res"},{"key":"1335_CR27","doi-asserted-by":"publisher","first-page":"101810","DOI":"10.1016\/j.compmedimag.2020.101810","volume":"87","author":"F Mohammed","year":"2021","unstructured":"Mohammed F, He X, Lin Y. An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson\u2019s disease using SPECT images. Comput Med Imaging Graph. 2021;87:101810. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101810.","journal-title":"Comput Med Imaging Graph"},{"key":"1335_CR28","unstructured":"Pianpanit T, et al. Neural network interpretation of the Parkinson\u2019s disease diagnosis from SPECT imaging. arXiv: Image and Video Processing. 2019;1\u20137."},{"key":"1335_CR29","first-page":"10","volume":"5","author":"CY Chien","year":"2023","unstructured":"Chien CY, Hsu SW, Lee TL, Sung PS, Lin CC. Using artificial neural network to discriminate Parkinson\u2019s disease from other parkinsonism\u2019s by focusing on putamen of dopamine transporter SPECT images: a retrospective study. Res Dev Med Med Sci. 2023;5:10\u201327.","journal-title":"Res Dev Med Med Sci"},{"key":"1335_CR30","unstructured":"Nalini TS, Anusha MU, Umarani K. Parkinson\u2019s disease detection using spect images and artificial neural network for classification. Int J Eng Res Technol (IJERT) IETE. 2020;8(11):105\u20138."},{"key":"1335_CR31","doi-asserted-by":"crossref","unstructured":"Kollia, Stafylopatis AG, Kollias S. Predicting Parkinson\u2019s disease using latent information extracted from deep neural networks. In 2019 international joint conference on neural networks. IEEE; 2019. p. 1\u20138.","DOI":"10.1109\/IJCNN.2019.8851995"},{"key":"1335_CR32","doi-asserted-by":"crossref","unstructured":"Rumman M, Tasneem AN, Farzana S, Pavel MI, Alam MA. Early detection of Parkinson\u2019s disease using image processing and artificial neural network, 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan. 2018. p. 256\u2013261.","DOI":"10.1109\/ICIEV.2018.8641081"},{"key":"1335_CR33","doi-asserted-by":"publisher","unstructured":"Mart\u00ednez-Murcia F, et al. A 3D convolutional neural network approach for the diagnosis of Parkinson\u2019s disease. In: International work conference on the interplay between natural and artificial computation, Springer;2017. p. 324\u2013333. https:\/\/doi.org\/10.1007\/978-3-319-59740-9_32.","DOI":"10.1007\/978-3-319-59740-9_32"},{"key":"1335_CR34","unstructured":"MJFF. The Michael J Fox Foundation for Parkinson\u2019s Research [WWW Document], 13 November 2022. https:\/\/www.michaeljfox.org."},{"issue":"4","key":"1335_CR35","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.pneurobio.2011.09.005","volume":"95","author":"K Marek","year":"2011","unstructured":"Marek K, et al. The Parkinson progression marker initiative (ppmi). Prog Neurobiol. 2011;95(4):629\u201335.","journal-title":"Prog Neurobiol"},{"key":"1335_CR36","doi-asserted-by":"publisher","unstructured":"Srinivas K, Sri R, Pravallika K, Nishitha K, Polamuri D. COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images, Multimedia Tools and Application. 2023. p. 1\u201318. https:\/\/doi.org\/10.1007\/s11042-023-15903-y.","DOI":"10.1007\/s11042-023-15903-y"},{"key":"1335_CR37","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). 2016. p. 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308.","DOI":"10.1109\/CVPR.2016.308"},{"issue":"8","key":"1335_CR38","first-page":"6280","volume":"34","author":"R Mohakud","year":"2021","unstructured":"Mohakud R, Dash R. Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection. J King Saud Univ Comput Inf Sci. 2021;34(8):6280\u201391.","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"1335_CR39","volume-title":"Data mining : concepts and techniques","author":"J Han","year":"2011","unstructured":"Han J, Pei J, Kamber M. Data mining\u202f: concepts and techniques. Elsevier: Morgan Kaufmann Publishers; 2011."},{"key":"1335_CR40","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Weiss R, Brucher M. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"1335_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/THC-174548","volume":"26","author":"H Lei","year":"2018","unstructured":"Lei H, et al. Sparse feature learning for multi-class Parkinson\u2019s disease classification. Technol Health Care. 2018;26:1\u201311.","journal-title":"Technol Health Care"},{"key":"1335_CR42","first-page":"12","volume":"21","author":"P Ramamurthy","year":"2019","unstructured":"Ramamurthy P, Rajakumar MP, Ramya J, Venkatesan R. Evaluation and classification of the brain tumor MRI using machine learning technique. Control Eng Appl Inform. 2019;21:12\u201321.","journal-title":"Control Eng Appl Inform"},{"key":"1335_CR43","doi-asserted-by":"publisher","unstructured":"Siddiqi MH, et al. A precise medical imaging approach for brain MRI image classification. Comput Intell Neurosci. 2022;2022(6447769):1\u201315. https:\/\/doi.org\/10.1155\/2022\/6447769.","DOI":"10.1155\/2022\/6447769"},{"key":"1335_CR44","doi-asserted-by":"publisher","first-page":"48","DOI":"10.3389\/fninf.2019.00048","volume":"13","author":"J Ortiz","year":"2019","unstructured":"Ortiz J, et al. Parkinson\u2019s disease detection using isosurfaces-based features and convolutional neural networks. Front Neuroinform. 2019;13:48.","journal-title":"Front Neuroinform."}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01335-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-024-01335-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01335-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T01:01:36Z","timestamp":1719190896000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-024-01335-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,24]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1335"],"URL":"https:\/\/doi.org\/10.1186\/s12880-024-01335-z","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,24]]},"assertion":[{"value":"18 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"156"}}