{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T11:11:34Z","timestamp":1781694694881,"version":"3.54.5"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"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":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00686-y","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T10:37:15Z","timestamp":1765363035000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Tealeafnet-gwo: an intelligent CNN-Transformer hybrid framework for tea leaf disease detection using gray wolf optimization"],"prefix":"10.1007","volume":"5","author":[{"given":"Md Firoz","family":"Kabir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irfan Sadiq","family":"Rahat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles","family":"Beverley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roise","family":"Uddin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shashi","family":"Kant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"686_CR1","first-page":"103784","volume":"25","author":"A Balasundaram","year":"2025","unstructured":"Balasundaram A, et al. Tea leaf disease detection using segment anything model and deep convolutional neural networks. Res Eng. 2025;25:103784.","journal-title":"Res Eng"},{"key":"686_CR2","doi-asserted-by":"publisher","first-page":"2435","DOI":"10.1007\/s00521-024-10758-2","volume":"37","author":"KG Panchbhai","year":"2025","unstructured":"Panchbhai KG, Lanjewar MG. Enhancement of tea leaf diseases identification using modified sota models. Neural Comput Appl. 2025;37:2435\u201353.","journal-title":"Neural Comput Appl"},{"key":"686_CR3","doi-asserted-by":"crossref","unstructured":"Sivaraman, R., Praveena, S. & Naresh\u00a0Kumar, H. Sustainable agriculture through advanced crop management: Vgg16-based tea leaf disease recognition. Generative Artificial Intelligence for Biomedical and Smart Health Informatics 121\u2013133 (2025).","DOI":"10.1002\/9781394280735.ch7"},{"key":"686_CR4","doi-asserted-by":"publisher","first-page":"109825","DOI":"10.1016\/j.compag.2024.109825","volume":"229","author":"Y Jiang","year":"2025","unstructured":"Jiang Y, Wei Z, Hu G. An efficient detection method for tea leaf blight in uav remote sensing images under intense lighting conditions based on mldnet. Comput Electron Agric. 2025;229:109825.","journal-title":"Comput Electron Agric"},{"key":"686_CR5","first-page":"101940","volume":"61","author":"J Liang","year":"2025","unstructured":"Liang J, Liang R, Wang D. A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism. Eng Sci Technol Int J. 2025;61:101940.","journal-title":"Eng Sci Technol Int J"},{"key":"686_CR6","unstructured":"Chowdhury, M. J.\u00a0U., Mou, Z.\u00a0I., Afrin, R. & Kibria, S. Plant leaf disease detection and classification using deep learning: a review and a proposed system on bangladesh\u2019s perspective. arXiv preprint arXiv:2501.03305 (2025)."},{"key":"686_CR7","doi-asserted-by":"publisher","first-page":"1503033","DOI":"10.3389\/fpls.2024.1503033","volume":"15","author":"X Wang","year":"2025","unstructured":"Wang X, Wu Z, Xiao G, Han C, Fang C. Yolov7-dws: tea bud recognition and detection network in multi-density environment via improved yolov7. Front Plant Sci. 2025;15:1503033.","journal-title":"Front Plant Sci"},{"key":"686_CR8","doi-asserted-by":"publisher","first-page":"100197","DOI":"10.1016\/j.fraope.2024.100197","volume":"10","author":"CB Nwaneto","year":"2025","unstructured":"Nwaneto CB, Yinka-Banjo C, Ugot O. An object detection solution for early detection of taro leaf blight disease in the west african sub-region. Franklin Open. 2025;10:100197.","journal-title":"Franklin Open"},{"key":"686_CR9","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/ppa.14006","volume":"74","author":"A Dolatabadian","year":"2025","unstructured":"Dolatabadian A, et al. Image-based crop disease detection using machine learning. Plant Pathol. 2025;74:18\u201338.","journal-title":"Plant Pathol"},{"key":"686_CR10","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s00521-024-10572-w","volume":"37","author":"STY Ramadan","year":"2025","unstructured":"Ramadan STY, et al. Image-based rice leaf disease detection using cnn and generative adversarial network. Neural Comput Appl. 2025;37:439\u201356.","journal-title":"Neural Comput Appl"},{"key":"686_CR11","doi-asserted-by":"publisher","first-page":"1489655","DOI":"10.3389\/fpls.2025.1489655","volume":"16","author":"L Li","year":"2025","unstructured":"Li L, Zhao Y. Tea disease identification based on eca attention mechanism resnet50 network. Front Plant Sci. 2025;16:1489655.","journal-title":"Front Plant Sci"},{"key":"686_CR12","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1080\/01140671.2024.2385813","volume":"53","author":"H Paul","year":"2025","unstructured":"Paul H, et al. Maize leaf disease detection using convolutional neural network: a mobile application based on pre-trained vgg16 architecture. N Z J Crop Hortic Sci. 2025;53:367\u201383.","journal-title":"N Z J Crop Hortic Sci"},{"key":"686_CR13","doi-asserted-by":"crossref","unstructured":"Tiwari, M. & Dev, H. Advances in deep learning techniques for plant disease identification: a comprehensive survey. In AIP Conference Proceedings, vol. 3224 (AIP Publishing, 2025).","DOI":"10.1063\/5.0245923"},{"key":"686_CR14","doi-asserted-by":"publisher","first-page":"7969","DOI":"10.1038\/s41598-025-92143-0","volume":"15","author":"Y Alhwaiti","year":"2025","unstructured":"Alhwaiti Y, et al. Leveraging yolo deep learning models to enhance plant disease identification. Sci Rep. 2025;15:7969.","journal-title":"Sci Rep"},{"key":"686_CR15","doi-asserted-by":"publisher","first-page":"107002","DOI":"10.1016\/j.cropro.2024.107002","volume":"189","author":"H-T Thai","year":"2025","unstructured":"Thai H-T, Le K-H. Mobileh-transformer: enabling real-time leaf disease detection using hybrid deep learning approach for smart agriculture. Crop Prot. 2025;189:107002.","journal-title":"Crop Prot"},{"key":"686_CR16","first-page":"9185","volume":"84","author":"T Varma","year":"2025","unstructured":"Varma T, Mate P, Azeem NA, Sharma S, Singh B. Automatic mango leaf disease detection using different transfer learning models. Mult Tools Appl. 2025;84:9185\u2013218.","journal-title":"Mult Tools Appl"},{"key":"686_CR17","doi-asserted-by":"publisher","first-page":"332","DOI":"10.3390\/agriculture15030332","volume":"15","author":"Z Lv","year":"2025","unstructured":"Lv Z, et al. Efficient deployment of peanut leaf disease detection models on edge ai devices. Agriculture. 2025;15:332.","journal-title":"Agriculture"},{"key":"686_CR18","doi-asserted-by":"publisher","first-page":"1467811","DOI":"10.3389\/fpls.2024.1467811","volume":"15","author":"A Chelladurai","year":"2025","unstructured":"Chelladurai A, Manoj Kumar D, Askar S, Abouhawwash M. Classification of tomato leaf disease using transductive long short-term memory with an attention mechanism. Front Plant Sci. 2025;15:1467811.","journal-title":"Front Plant Sci"},{"key":"686_CR19","doi-asserted-by":"publisher","unstructured":"Ghosh, S. & Singh, A. The analysis of plants image classification based on machine learning approaches. In Emergent Converging Technologies and Biomedical Systems, vol. 841 of Lecture Notes in Electrical Engineering, https:\/\/doi.org\/10.1007\/978-981-16-8774-7_12 (Springer, Singapore, 2022).","DOI":"10.1007\/978-981-16-8774-7_12"},{"key":"686_CR20","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.matpr.2023.02.370","volume":"80","author":"J Kotwal","year":"2023","unstructured":"Kotwal J, Kashyap R, Pathan S. Agricultural plant diseases identification: From traditional approach to deep learning. Mater Today Proceed. 2023;80:344\u201356. https:\/\/doi.org\/10.1016\/j.matpr.2023.02.370.","journal-title":"Mater Today Proceed"},{"key":"686_CR21","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.11591\/ijeecs.v31.i3.pp1605-1615","volume":"31","author":"S Ghosh","year":"2023","unstructured":"Ghosh S, Singh A, Kumar S. Identification of medicinal plant using hybrid transfer learning technique. Indonesian J Electric Eng Comput Sci. 2023;31:1605\u201315. https:\/\/doi.org\/10.11591\/ijeecs.v31.i3.pp1605-1615.","journal-title":"Indonesian J Electric Eng Comput Sci"},{"key":"686_CR22","doi-asserted-by":"publisher","first-page":"38209","DOI":"10.1007\/s11042-023-16882-w","volume":"83","author":"JG Kotwal","year":"2024","unstructured":"Kotwal JG, Kashyap R, Shafi PM. Artificial driving based efficientnet for automatic plant leaf disease classification. Multi Tools Appl. 2024;83:38209\u201340. https:\/\/doi.org\/10.1007\/s11042-023-16882-w.","journal-title":"Multi Tools Appl"},{"key":"686_CR23","doi-asserted-by":"publisher","first-page":"4375","DOI":"10.1007\/s41870-023-01472-8","volume":"15","author":"S Ghosh","year":"2023","unstructured":"Ghosh S, Singh A, Kumar S. Bbbc-u-net: optimizing u-net for automated plant phenotyping using big bang big crunch global optimization algorithm. Int J Inf Technol. 2023;15:4375\u201387. https:\/\/doi.org\/10.1007\/s41870-023-01472-8.","journal-title":"Int J Inf Technol"},{"key":"686_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jspr.2024.102314","volume":"107","author":"JG Kotwal","year":"2024","unstructured":"Kotwal JG, et al. A modified time adaptive self-organizing map with stochastic gradient descent optimizer for automated food recognition system. J Stored Prod Res. 2024;107:102314. https:\/\/doi.org\/10.1016\/j.jspr.2024.102314.","journal-title":"J Stored Prod Res"},{"key":"686_CR25","doi-asserted-by":"publisher","first-page":"35263","DOI":"10.1007\/s11042-019-08094-y","volume":"78","author":"SS Chouhan","year":"2019","unstructured":"Chouhan SS, Kaul A, Singh UP. Image segmentation using fuzzy competitive learning based counter propagation network. Multi Tools Appl. 2019;78:35263\u201387. https:\/\/doi.org\/10.1007\/s11042-019-08094-y.","journal-title":"Multi Tools Appl"},{"key":"686_CR26","doi-asserted-by":"publisher","first-page":"79750","DOI":"10.1109\/ACCESS.2023.3298955","volume":"11","author":"AA Abdelhamid","year":"2023","unstructured":"Abdelhamid AA, et al. Innovative feature selection method based on hybrid sine cosine and dipper throated optimization algorithms. IEEE Access. 2023;11:79750\u201376. https:\/\/doi.org\/10.1109\/ACCESS.2023.3298955.","journal-title":"IEEE Access"},{"key":"686_CR27","doi-asserted-by":"publisher","first-page":"23681","DOI":"10.1109\/ACCESS.2023.3253430","volume":"11","author":"M Hadjouni","year":"2023","unstructured":"Hadjouni M, et al. Advanced meta-heuristic algorithm based on particle swarm and al-biruni earth radius optimization methods for oral cancer detection. IEEE Access. 2023;11:23681\u2013700. https:\/\/doi.org\/10.1109\/ACCESS.2023.3253430.","journal-title":"IEEE Access"},{"key":"686_CR28","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.32604\/cmc.2023.031723","volume":"75","author":"G Atteia","year":"2023","unstructured":"Atteia G, et al. Adaptive dynamic dipper throated optimization for feature selection in medical data. Comput Mater Continua. 2023;75:1883\u2013900. https:\/\/doi.org\/10.32604\/cmc.2023.031723.","journal-title":"Comput Mater Continua"},{"key":"686_CR29","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1504\/IJISTA.2024.140949","volume":"22","author":"JG Kotwal","year":"2024","unstructured":"Kotwal JG, Kashyap R, Pathan MS. Yolov5-based convolutional feature attention neural network for plant disease classification. Int J Intell Syst Technol Appl. 2024;22:237\u201359. https:\/\/doi.org\/10.1504\/IJISTA.2024.140949.","journal-title":"Int J Intell Syst Technol Appl"},{"key":"686_CR30","doi-asserted-by":"publisher","first-page":"4531","DOI":"10.1007\/s00521-024-10830-x","volume":"37","author":"SS Chouhan","year":"2025","unstructured":"Chouhan SS, Singh UP, Jain S. Performance evaluation of different deep learning models used for the purpose of healthy and diseased leaves classification of cherimoya (annona cherimola) plant. Neural Comput Appl. 2025;37:4531\u201344. https:\/\/doi.org\/10.1007\/s00521-024-10830-x.","journal-title":"Neural Comput Appl"},{"key":"686_CR31","doi-asserted-by":"publisher","first-page":"15","DOI":"10.4114\/intartif.vol26iss72","volume":"26","author":"S Ghosh","year":"2023","unstructured":"Ghosh S, Singh A, Kumar S. Pb3c-cnn: An integrated pb3c and cnn based approach for plant leaf classification. Intel Artif. 2023;26:15\u201329. https:\/\/doi.org\/10.4114\/intartif.vol26iss72.","journal-title":"Intel Artif"},{"key":"686_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2024.110216","volume":"53","author":"J Kotwal","year":"2024","unstructured":"Kotwal J, Kashyap R, Pathan MS. An india soyabean dataset for identification and classification of diseases using computer-vision algorithms. Data Brief. 2024;53:110216. https:\/\/doi.org\/10.1016\/j.dib.2024.110216.","journal-title":"Data Brief"},{"key":"686_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s42044-025-00271-7","author":"JG Kotwal","year":"2025","unstructured":"Kotwal JG, et al. Sadccnet: self-attention-based dense cascaded capsule network for bone cancer detection using deep learning approach. Iran J Comput Scince. 2025. https:\/\/doi.org\/10.1007\/s42044-025-00271-7.","journal-title":"Iran J Comput Scince"},{"key":"686_CR34","doi-asserted-by":"publisher","first-page":"5949","DOI":"10.3390\/su15075949","volume":"15","author":"SM Abdullah","year":"2023","unstructured":"Abdullah SM, et al. Optimizing traffic flow in smart cities: soft gru-based recurrent neural networks for enhanced congestion prediction using deep learning. Sustainability. 2023;15:5949. https:\/\/doi.org\/10.3390\/su15075949.","journal-title":"Sustainability"},{"key":"686_CR35","doi-asserted-by":"publisher","first-page":"94094","DOI":"10.1109\/ACCESS.2023.3310429","volume":"11","author":"N Khodadadi","year":"2023","unstructured":"Khodadadi N, Mirjalili S, et al. Baoa: binary arithmetic optimization algorithm with k-nearest neighbor classifier for feature selection. IEEE Access. 2023;11:94094\u2013115. https:\/\/doi.org\/10.1109\/ACCESS.2023.3310429.","journal-title":"IEEE Access"},{"key":"686_CR36","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/s43926-025-00168-8","volume":"5","author":"S Ghosh","year":"2025","unstructured":"Ghosh S, Singh A, Kumar S. Multiplier leadership optimization algorithm (mloa): unconstrained global optimization approach for melanoma classification. Discov Int Thing. 2025;5:70. https:\/\/doi.org\/10.1007\/s43926-025-00168-8.","journal-title":"Discov Int Thing"},{"key":"686_CR37","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s44196-025-00833-4","volume":"18","author":"J Kotwal","year":"2025","unstructured":"Kotwal J, Futane P, Chavan G, et al. Sensor infused quantum cnn for diabetes disease prediction and diet recommendation. Int J Comput Intell Syst. 2025;18:113. https:\/\/doi.org\/10.1007\/s44196-025-00833-4.","journal-title":"Int J Comput Intell Syst"},{"key":"686_CR38","doi-asserted-by":"publisher","unstructured":"Dongre P, Kedia S, Banubakade J, & Kotambkar DM. Optimizing workforce efficiency using an artificial intelligence approach: A next-gen hr management system. In 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 1416\u20131421, https:\/\/doi.org\/10.1109\/ICETSIS61505.2024.10459590 (Manama, Bahrain, 2024).","DOI":"10.1109\/ICETSIS61505.2024.10459590"},{"key":"686_CR39","doi-asserted-by":"publisher","first-page":"84188","DOI":"10.1109\/ACCESS.2022.3196660","volume":"10","author":"AA Alhussan","year":"2022","unstructured":"Alhussan AA, et al. Pothole and plain road classification using adaptive mutation dipper throated optimization and transfer learning for self driving cars. IEEE Access. 2022;10:84188\u2013211. https:\/\/doi.org\/10.1109\/ACCESS.2022.3196660.","journal-title":"IEEE Access"},{"key":"686_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2024.102581","volume":"81","author":"S Ghosh","year":"2024","unstructured":"Ghosh S, Singh A, Kumar S. Hpb3c-3pg algorithm: a new hybrid global optimization algorithm and its application to plant classification. Eco Inform. 2024;81:102581. https:\/\/doi.org\/10.1016\/j.ecoinf.2024.102581.","journal-title":"Eco Inform"},{"key":"686_CR41","doi-asserted-by":"publisher","first-page":"2892","DOI":"10.3390\/diagnostics12112892","volume":"12","author":"DS Khafaga","year":"2022","unstructured":"Khafaga DS, et al. An al-biruni earth radius optimization-based deep convolutional neural network for classifying monkeypox disease. Diagnostics. 2022;12:2892. https:\/\/doi.org\/10.3390\/diagnostics12112892.","journal-title":"Diagnostics"},{"key":"686_CR42","doi-asserted-by":"publisher","unstructured":"Sharma A, Patel RK, Pranjal P, Panchal B, & Chouhan SS. Computer vision-based smart monitoring and control system for crop. In Applications of Computer Vision and Drone Technology in Agriculture 4.0, https:\/\/doi.org\/10.1007\/978-981-99-8684-2_5 (Springer, Singapore, 2024).","DOI":"10.1007\/978-981-99-8684-2_5"},{"key":"686_CR43","doi-asserted-by":"publisher","unstructured":"Chouhan SS, Singh UP, Saxena A, Jain S. Assessing the importance and need of artificial intelligence for precision agriculture. In Artificial Intelligence Techniques in Smart Agriculture https:\/\/doi.org\/10.1007\/978-981-97-5878-4_1 (Springer, Singapore 2024).","DOI":"10.1007\/978-981-97-5878-4_1"},{"key":"686_CR44","doi-asserted-by":"publisher","unstructured":"Ghosh S, & Singh A. Image classification using deep neural networks: Emotion detection using facial images. InRoy, M. &Gupta, L.\u00a0R. (eds.) Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease, 75\u201385, https:\/\/doi.org\/10.4018\/978-1-7998-7188-0.ch006 (IGI Global, Hershey, PA, 2021).","DOI":"10.4018\/978-1-7998-7188-0.ch006"},{"key":"686_CR45","doi-asserted-by":"publisher","first-page":"4421","DOI":"10.3390\/math10234421","volume":"10","author":"E-SM El-Kenawy","year":"2022","unstructured":"El-Kenawy E-SM, et al. Metaheuristic optimization for improving weed detection in wheat images captured by drones. Mathematics. 2022;10:4421. https:\/\/doi.org\/10.3390\/math10234421.","journal-title":"Mathematics"},{"key":"686_CR46","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.32604\/cmc.2023.033273","volume":"74","author":"R Alkanhel","year":"2023","unstructured":"Alkanhel R, et al. Network intrusion detection based on feature selection and hybrid metaheuristic optimization. Comput Mater Continua. 2023;74:2677\u201393. https:\/\/doi.org\/10.32604\/cmc.2023.033273.","journal-title":"Comput Mater Continua"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00686-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00686-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00686-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T11:04:31Z","timestamp":1765364671000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00686-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,10]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["686"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00686-y","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,10]]},"assertion":[{"value":"16 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 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":"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":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Institutional review board"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}},{"value":"The authors declare that they have no Conflict of interest.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"377"}}