{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:08:34Z","timestamp":1781280514949,"version":"3.54.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s00521-025-11014-x","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T18:15:16Z","timestamp":1738088116000},"page":"8479-8507","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Optimizing medical image analysis through MViTX on multiple datasets with explainable AI"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5845-5210","authenticated-orcid":false,"given":"Md. Alif","family":"Sheakh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2027-7148","authenticated-orcid":false,"given":"Mst. Sazia","family":"Tahosin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5573-8449","authenticated-orcid":false,"given":"Mohammad Jahangir","family":"Alam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1206-3669","authenticated-orcid":false,"given":"Mahbuba","family":"Begum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"11014_CR1","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1002\/IJC.33588","volume":"149","author":"J Ferlay","year":"2021","unstructured":"Ferlay J, Colombet M, Soerjomataram I et al (2021) Cancer statistics for the year 2020: an overview. Int J Cancer 149:778\u2013789. https:\/\/doi.org\/10.1002\/IJC.33588","journal-title":"Int J Cancer"},{"key":"11014_CR2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3322\/caac.21763","volume":"73","author":"RL Siegel Mph","year":"2023","unstructured":"Siegel Mph RL, Miller KD, Sandeep N et al (2023) Cancer statistics, 2023. CA Cancer J Clin 73:17\u201348. https:\/\/doi.org\/10.3322\/caac.21763","journal-title":"CA Cancer J Clin"},{"key":"11014_CR3","doi-asserted-by":"publisher","unstructured":"Kalogeropoulos D, Sakkas H, Mohammed B, et al (2021) Ocular toxoplasmosis: a review of the current diagnostic and therapeutic approaches. Int Ophthalmol 42(1):295\u2013321. https:\/\/doi.org\/10.1007\/S10792-021-01994-9","DOI":"10.1007\/S10792-021-01994-9"},{"key":"11014_CR4","doi-asserted-by":"publisher","unstructured":"Ahmad M, Capitena CE, Curtis D, McCourt EA (2017) Ocular manifestations of infectious diseases. The eye in pediatric systemic disease, pp 327\u2013357. https:\/\/doi.org\/10.1007\/978-3-319-18389-3_12\/COVER","DOI":"10.1007\/978-3-319-18389-3_12\/COVER"},{"key":"11014_CR5","doi-asserted-by":"publisher","DOI":"10.3389\/FONC.2022.890908\/BIBTEX","volume":"12","author":"K Njoku","year":"2022","unstructured":"Njoku K, Barr CE, Crosbie EJ (2022) Current and emerging prognostic biomarkers in endometrial cancer. Front Oncol 12:890908. https:\/\/doi.org\/10.3389\/FONC.2022.890908\/BIBTEX","journal-title":"Front Oncol"},{"key":"11014_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S12032-023-02040-7\/METRICS","volume":"40","author":"AC Jones","year":"2023","unstructured":"Jones AC, Brown KH, Guan T et al (2023) The past, present, and future of immunotherapy for endometrial adenocarcinoma. Med Oncol 40:1\u20136. https:\/\/doi.org\/10.1007\/S12032-023-02040-7\/METRICS","journal-title":"Med Oncol"},{"key":"11014_CR7","doi-asserted-by":"publisher","first-page":"233","DOI":"10.3322\/CAAC.21772","volume":"73","author":"RL Siegel Mph","year":"2023","unstructured":"Siegel Mph RL, Sandeep N, Mbbs W et al (2023) Colorectal cancer statistics, 2023. CA Cancer J Clin 73:233\u2013254. https:\/\/doi.org\/10.3322\/CAAC.21772","journal-title":"CA Cancer J Clin"},{"key":"11014_CR8","doi-asserted-by":"publisher","unstructured":"Guo S, Chen M, Li S et al (2023) Natural products treat colorectal cancer by regulating miRNA. Pharmaceuticals 16:1122. https:\/\/doi.org\/10.3390\/PH16081122","DOI":"10.3390\/PH16081122"},{"key":"11014_CR9","doi-asserted-by":"publisher","first-page":"1036498","DOI":"10.3389\/FPHAR.2022.1036498\/BIBTEX","volume":"13","author":"B Ni","year":"2022","unstructured":"Ni B, Song X, Shi B et al (2022) Research progress of ginseng in the treatment of gastrointestinal cancers. Front Pharmacol 13:1036498. https:\/\/doi.org\/10.3389\/FPHAR.2022.1036498\/BIBTEX","journal-title":"Front Pharmacol"},{"key":"11014_CR10","doi-asserted-by":"publisher","first-page":"52","DOI":"10.2188\/JEA.JE20190242","volume":"31","author":"Y Taniyama","year":"2021","unstructured":"Taniyama Y, Tabuchi T, Ohno Y et al (2021) Hospital surgical volume and 3-year mortality in severe prognosis cancers: a population-based study using cancer registry data. J Epidemiol 31:52\u201358. https:\/\/doi.org\/10.2188\/JEA.JE20190242","journal-title":"J Epidemiol"},{"key":"11014_CR11","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1016\/J.SOC.2023.05.008","volume":"32","author":"T Wang","year":"2023","unstructured":"Wang T, Dossett LA (2023) Incorporating value-based decisions in breast cancer treatment algorithms. Surg Oncol Clin 32:777\u2013797. https:\/\/doi.org\/10.1016\/J.SOC.2023.05.008","journal-title":"Surg Oncol Clin"},{"key":"11014_CR12","doi-asserted-by":"publisher","unstructured":"Liu KQT, Dallas J, Wenger TA, et al (2023) Coronavirus disease-19 is associated with decreased treatment access and worsened outcomes in malignant brain tumor patients. Surg Neurol Int 14. https:\/\/doi.org\/10.25259\/SNI_440_2023","DOI":"10.25259\/SNI_440_2023"},{"key":"11014_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/J.IMU.2023.101414","volume":"43","author":"MS Tahosin","year":"2023","unstructured":"Tahosin MS, Sheakh MA, Islam T et al (2023) Optimizing brain tumor classification through feature selection and hyperparameter tuning in machine learning models. Inform Med Unlocked 43:101414. https:\/\/doi.org\/10.1016\/J.IMU.2023.101414","journal-title":"Inform Med Unlocked"},{"key":"11014_CR14","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1097\/AUD.0000000000001037","volume":"42","author":"GR Merchant","year":"2021","unstructured":"Merchant GR, Al-Salim S, Tempero RM et al (2021) Improving the differential diagnosis of otitis media with effusion using wideband acoustic immittance. Ear Hear 42:1183. https:\/\/doi.org\/10.1097\/AUD.0000000000001037","journal-title":"Ear Hear"},{"key":"11014_CR15","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/J.UROLOGY.2021.10.008","volume":"166","author":"MM Kim","year":"2022","unstructured":"Kim MM, Harvey J, Gusev A et al (2022) A scoping review of the economic burden of non-cancerous genitourinary conditions. Urology 166:29\u201338. https:\/\/doi.org\/10.1016\/J.UROLOGY.2021.10.008","journal-title":"Urology"},{"key":"11014_CR16","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-33128-3_1\/COVER","volume":"1213","author":"HP Chan","year":"2020","unstructured":"Chan HP, Samala RK, Hadjiiski LM, Zhou C (2020) Deep learning in medical image analysis. Adv Exp Med Biol 1213:3\u201321. https:\/\/doi.org\/10.1007\/978-3-030-33128-3_1\/COVER","journal-title":"Adv Exp Med Biol"},{"key":"11014_CR17","doi-asserted-by":"publisher","unstructured":"Abhisheka B, Biswas SK, Purkayastha B, et al (2023) Recent trend in medical imaging modalities and their applications in disease diagnosis: a review. Multimed Tools Appl, pp 1\u201336. https:\/\/doi.org\/10.1007\/S11042-023-17326-1\/METRICS","DOI":"10.1007\/S11042-023-17326-1\/METRICS"},{"key":"11014_CR18","doi-asserted-by":"publisher","unstructured":"Liu X, Gao K, Liu B, et al (2021) Advances in deep learning-based medical image analysis. Health Data Sci. https:\/\/doi.org\/10.34133\/2021\/8786793","DOI":"10.34133\/2021\/8786793"},{"key":"11014_CR19","doi-asserted-by":"publisher","unstructured":"Iqbal S, N. Qureshi A, Li J, Mahmood T (2023) On the analyses of medical images using traditional machine learning techniques and convolutional neural networks. Arch Comput Methods Eng 30(5):3173\u20133233. https:\/\/doi.org\/10.1007\/S11831-023-09899-9","DOI":"10.1007\/S11831-023-09899-9"},{"key":"11014_CR20","doi-asserted-by":"publisher","first-page":"8487","DOI":"10.1038\/s41598-024-57740-5","volume":"14","author":"T Islam","year":"2024","unstructured":"Islam T, Sheakh MdA, MstS T et al (2024) Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI. Sci Rep 14:8487. https:\/\/doi.org\/10.1038\/s41598-024-57740-5","journal-title":"Sci Rep"},{"key":"11014_CR21","doi-asserted-by":"publisher","unstructured":"Rahad Islam Bhuiyan M, Azam S, Montaha S, et al (2023) Deep learning-based analysis of COVID-19 X-ray images: incorporating clinical significance and assessing misinterpretation. journals.sagepub.comMR Islam Bhuiyan, S Azam, S Montaha, RI Jim, A Karim, IU Khan, M Brady, MZ HasanDigital Health, 2023\u2022journals.sagepub.com 9:. https:\/\/doi.org\/10.1177\/20552076231215915","DOI":"10.1177\/20552076231215915"},{"key":"11014_CR22","doi-asserted-by":"publisher","unstructured":"Sultana Chowa S, Azam S, Montaha S, Payel IJ, Bhuiyan MRI, Hasan MZ, Jonkman M et al (2023) Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features. J Cancer Res Clin Oncol. Springer 149:18039\u201318064. https:\/\/doi.org\/10.1007\/s00432-023-05464-w","DOI":"10.1007\/s00432-023-05464-w"},{"key":"11014_CR23","doi-asserted-by":"publisher","DOI":"10.3389\/FMED.2022.924979\/BIBTEX","volume":"9","author":"S Montaha","year":"2022","unstructured":"Montaha S, Azam S, Rafid AKMRH et al (2022) MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique. Front Med (Lausanne) 9:924979. https:\/\/doi.org\/10.3389\/FMED.2022.924979\/BIBTEX","journal-title":"Front Med (Lausanne)"},{"key":"11014_CR24","doi-asserted-by":"publisher","unstructured":"Abid MH, Ashraf Id R, Mahmood T, et al (2023) Multi-modal medical image classification using deep residual network and genetic algorithm. J Plos One 18. https:\/\/doi.org\/10.1371\/journal.pone.0287786","DOI":"10.1371\/journal.pone.0287786"},{"key":"11014_CR25","doi-asserted-by":"publisher","unstructured":"Ghodeswar U, Borkar A, Bagde A (2022) Classification of different medical images using neural network approach. Indian J Sci Technol 15:2555\u20132561. https:\/\/doi.org\/10.17485\/IJST\/V15I46.1160","DOI":"10.17485\/IJST\/V15I46.1160"},{"key":"11014_CR26","doi-asserted-by":"publisher","unstructured":"El-Shafai W, Mahmoud AA, Ali AM, El-Rabaie ESM, Taha TE, El-Fishawy AS, Zahranet O et al. (2023) Efficient classification of different medical image multimodalities based on simple CNN architecture and augmentation algorithms. J Opt. Springer. https:\/\/doi.org\/10.1007\/s12596-022-01089-3","DOI":"10.1007\/s12596-022-01089-3"},{"key":"11014_CR27","doi-asserted-by":"crossref","unstructured":"Ashraf R, Habib MA, Akram M, Latif MA, Malik MSA, Awais M, Dar SH, Mahmood T, Yasir M et al. (2020) Deep convolution neural network for big data medical image classification. IEEE Access","DOI":"10.1109\/ACCESS.2020.2998808"},{"key":"11014_CR28","doi-asserted-by":"publisher","DOI":"10.3389\/FMED.2021.629134\/BIBTEX","volume":"8","author":"M Elgendi","year":"2021","unstructured":"Elgendi M, Nasir MU, Tang Q et al (2021) The effectiveness of image augmentation in deep learning networks for detecting COVID-19: a geometric transformation perspective. Front Med (Lausanne) 8:629134. https:\/\/doi.org\/10.3389\/FMED.2021.629134\/BIBTEX","journal-title":"Front Med (Lausanne)"},{"key":"11014_CR29","doi-asserted-by":"publisher","unstructured":"Miko\u0142ajczyk A, MGP workshop, 2018 undefined (2018) Data augmentation for improving deep learning in image classification problem. ieeexplore.ieee.org. https:\/\/doi.org\/10.1109\/IIPHDW.2018.8388338","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"11014_CR30","unstructured":"Hussain Z, Gimenez F, Yi D, et al (2017) Differential data augmentation techniques for medical imaging classification tasks. In: AMIA annual symposium proceedings"},{"key":"11014_CR31","doi-asserted-by":"publisher","unstructured":"Cunha A, Salgado PAC, Perdico\u00falis TP, et al (2023) Explainable artificial intelligence (xai) for deep learning based medical imaging classification. J Imag. https:\/\/doi.org\/10.3390\/jimaging9090177","DOI":"10.3390\/jimaging9090177"},{"key":"11014_CR32","doi-asserted-by":"crossref","unstructured":"Ribeiro E, Cardenas D, Dias F, Krieger JE, Gutierrezmed MA et al. (2023) Explainable AI in deep learning-based detection of aortic elongation on chest X-ray images","DOI":"10.1101\/2023.08.28.23294735"},{"key":"11014_CR33","doi-asserted-by":"publisher","unstructured":"Aldughayfiq B, Ashfaq F, Jhanjhi N. Humayun M et al (2023) Explainable AI for retinoblastoma diagnosis: interpreting deep learning models with LIME and SHAP. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics13111932","DOI":"10.3390\/diagnostics13111932"},{"key":"11014_CR34","unstructured":"Ocular Toxoplasmosis Fundus Images Dataset. https:\/\/www.kaggle.com\/datasets\/nafin59\/ocular-toxoplasmosis-fundus-images-dataset. Accessed 6 Jan 2024"},{"key":"11014_CR35","unstructured":"A histopathological image dataset for endometrial disease diagnosis. https:\/\/figshare.com\/articles\/dataset\/A_histopathological_image_dataset_for_endometrial_disease_diagnosis\/7306361\/2. Accessed 6 Jan 2024"},{"key":"11014_CR36","doi-asserted-by":"publisher","unstructured":"Pet\u00e4inen L (2023) Histopathological image patches from colorectal cancer with three classes: tumor, stroma and other 1. https:\/\/doi.org\/10.17632\/37T2D6XMY2.1","DOI":"10.17632\/37T2D6XMY2.1"},{"key":"11014_CR37","unstructured":"Simula Datasets - Kvasir. https:\/\/datasets.simula.no\/kvasir\/. Accessed 6 Jan 2024"},{"key":"11014_CR38","unstructured":"BreaKHis 400X. https:\/\/www.kaggle.com\/datasets\/forderation\/breakhis-400x. Accessed 6 Jan 2024"},{"key":"11014_CR39","unstructured":"Brain Tumor Dataset. https:\/\/www.scidb.cn\/en\/detail?dataSetId=faa44e0a12da4c11aeee91cc3c8ac11e. Accessed 6 Jan 2024"},{"key":"11014_CR40","unstructured":"Tympanic membrane \/ eardrum dataset \/ otitis media. https:\/\/www.kaggle.com\/datasets\/erdalbasaran\/eardrum-dataset-otitis-media?select=eardrumDs. Accessed 6 Jan 2024"},{"key":"11014_CR41","doi-asserted-by":"publisher","first-page":"33438","DOI":"10.1109\/ACCESS.2021.3058773","volume":"9","author":"AR Beeravolu","year":"2021","unstructured":"Beeravolu AR, Azam S, Jonkman M et al (2021) Preprocessing of breast cancer images to create datasets for deep-CNN. IEEE Access 9:33438\u201333463. https:\/\/doi.org\/10.1109\/ACCESS.2021.3058773","journal-title":"IEEE Access"},{"key":"11014_CR42","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/J.IMAGE.2014.10.009","volume":"30","author":"N Ponomarenko","year":"2015","unstructured":"Ponomarenko N, Jin L, Ieremeiev O et al (2015) Image database TID2013: peculiarities, results and perspectives. Signal Process Image Commun 30:57\u201377. https:\/\/doi.org\/10.1016\/J.IMAGE.2014.10.009","journal-title":"Signal Process Image Commun"},{"key":"11014_CR43","unstructured":"Zhang H, Hao K, Pedrycz W, et al (2022) Vision Transformer with Convolutions Architecture Search"},{"key":"11014_CR44","doi-asserted-by":"publisher","unstructured":"Khan RF, Lee B-D, Lee MS (2023) Transformers in medical image segmentation: a narrative review. Quant Imag Med Surg 13:8747. https:\/\/doi.org\/10.21037\/QIMS-23-542","DOI":"10.21037\/QIMS-23-542"},{"key":"11014_CR45","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2023","unstructured":"Han K, Wang Y, Chen H et al (2023) A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell 45:87\u2013110. https:\/\/doi.org\/10.1109\/TPAMI.2022.3152247","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11014_CR46","doi-asserted-by":"publisher","unstructured":"Zhang S, Xu M, Zhou J, Jia S (2022) Unsupervised spatial-spectral CNN-based feature learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60. https:\/\/doi.org\/10.1109\/TGRS.2022.3153673","DOI":"10.1109\/TGRS.2022.3153673"},{"key":"11014_CR47","doi-asserted-by":"publisher","unstructured":"Singh A, Sengupta S, Lakshminarayanan V (2020) Explainable deep learning models in medical image analysis. J Imag 6:52. https:\/\/doi.org\/10.3390\/JIMAGING6060052","DOI":"10.3390\/JIMAGING6060052"},{"key":"11014_CR48","doi-asserted-by":"publisher","unstructured":"Arun N, Gaw N, Singh P, et al (2021) Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging. Radiol Artif Intell 3. https:\/\/doi.org\/10.1148\/RYAI.2021200267\/ASSET\/IMAGES\/LARGE\/RYAI.2021200267TBL4.JPEG","DOI":"10.1148\/RYAI.2021200267\/ASSET\/IMAGES\/LARGE\/RYAI.2021200267TBL4.JPEG"},{"key":"11014_CR49","doi-asserted-by":"publisher","first-page":"11","DOI":"10.4236\/JCC.2024.121002","volume":"12","author":"T Islam","year":"2024","unstructured":"Islam T, Sheakh MdA, Sadik MdR et al (2024) Lexicon and deep learning-based approaches in sentiment analysis on short texts. J Comput Commun 12:11\u201334. https:\/\/doi.org\/10.4236\/JCC.2024.121002","journal-title":"J Comput Commun"},{"key":"11014_CR50","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1002\/SIM.1180","volume":"21","author":"HC Kraemer","year":"2002","unstructured":"Kraemer HC, Periyakoil VS, Noda A (2002) Kappa coefficients in medical research. Stat Med 21:2109\u20132129. https:\/\/doi.org\/10.1002\/SIM.1180","journal-title":"Stat Med"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11014-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11014-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11014-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T08:13:33Z","timestamp":1746260013000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11014-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,28]]},"references-count":50,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["11014"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11014-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,28]]},"assertion":[{"value":"9 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 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 that they have no competing financial interests or personal relationships that may have influenced the work reported in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}