{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:38:58Z","timestamp":1761176338470,"version":"build-2065373602"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the National Key R&D Program of China","award":["2018YFA0701700","2018YFA0701700"],"award-info":[{"award-number":["2018YFA0701700","2018YFA0701700"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20170","62271337","62371328"],"award-info":[{"award-number":["U20A20170","62271337","62371328"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Agency for Science, Technology and Research (A*STAR) Career Development Fund","award":["C222812010"],"award-info":[{"award-number":["C222812010"]}]},{"name":"Agency for Science, Technology and Research (A*STAR) Central Research Fund"},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62371326"],"award-info":[{"award-number":["62371326"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Jiangsu Province of China","award":["BK20211308"],"award-info":[{"award-number":["BK20211308"]}]},{"DOI":"10.13039\/501100010226","name":"Department of Education of Guangdong Province","doi-asserted-by":"publisher","award":["2024ZDZX2024"],"award-info":[{"award-number":["2024ZDZX2024"]}],"id":[{"id":"10.13039\/501100010226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shantou Science and Technology Program","award":["190917085269835"],"award-info":[{"award-number":["190917085269835"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-01988-2","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T11:20:10Z","timestamp":1760959210000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep learning based automatic report generator for retinal optical coherence tomography images"],"prefix":"10.1038","volume":"8","author":[{"given":"Xinjian","family":"Chen","sequence":"first","affiliation":[]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Jingtao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Tian","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Cangxin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhongyue","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Aidi","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Anlin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Weifang","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Shirong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Dehui","family":"Xiang","sequence":"additional","affiliation":[]},{"given":"Baoqing","family":"Nie","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yuanyuan","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Danqi","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Mingzhi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chi Pui","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Haoyu","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"1988_CR1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1001\/jama.2023.26074","volume":"331","author":"M Fleckenstein","year":"2024","unstructured":"Fleckenstein, M., Schmitz-Valckenberg, S. & Chakravarthy, U. Age-related macular degeneration: a review. JAMA 331, 147\u2013157 (2024).","journal-title":"JAMA"},{"key":"1988_CR2","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1136\/bjophthalmol-2017-310316","volume":"101","author":"G Zhang","year":"2017","unstructured":"Zhang, G., Chen, H., Chen, W. & Zhang, M. Prevalence and risk factors for diabetic retinopathy in china: a multi-hospital- based cross-sectional study. Br. J. Ophthalmol. 101, 1591\u20131595 (2017).","journal-title":"Br. J. Ophthalmol."},{"key":"1988_CR3","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1126\/science.1957169","volume":"254","author":"D Huang","year":"1991","unstructured":"Huang, D. et al. Optical coherence tomography. Science 254, 1178\u20131181 (1991).","journal-title":"Science"},{"key":"1988_CR4","doi-asserted-by":"publisher","first-page":"5604","DOI":"10.3390\/jcm11195604","volume":"11","author":"A Lin","year":"2022","unstructured":"Lin, A. et al. Research trends and hotspots of retinal optical coherence tomography: a 31\u2009year bibliometric analysis. J. Clin. Med. 11, 5604 (2022).","journal-title":"J. Clin. Med."},{"key":"1988_CR5","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"A Khan","year":"2020","unstructured":"Khan, A., Sohail, A., Zahoora, U. & Qureshi, A. S. A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53, 5455\u20135516 (2020).","journal-title":"Artif. Intell. Rev."},{"key":"1988_CR6","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.3390\/e23121651","volume":"23","author":"PD Barua","year":"2021","unstructured":"Barua, P. D. et al. Multilevel deep feature generation framework for automated detection of retinal abnormalities using oct images. Entropy 23, 1651 (2021).","journal-title":"Entropy"},{"key":"1988_CR7","doi-asserted-by":"publisher","unstructured":"Shen, E. et al. DRFNet: a deep radiomic fusion network for nAMD\/PCV differentiation in oct images. Phys. Medicine Biol. https:\/\/doi.org\/10.1088\/1361-6560\/ad2ca0 (2024).","DOI":"10.1088\/1361-6560\/ad2ca0"},{"key":"1988_CR8","doi-asserted-by":"crossref","unstructured":"Wang, M. et al. Self-guided optimization semi-supervised method for joint segmentation of macular hole and cystoid macular edema in retinal oct images. In IEEE Transactions on Biomedical Engineering, 70 (2023).","DOI":"10.1109\/TBME.2023.3234031"},{"key":"1988_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.688986","volume":"8","author":"H Huang","year":"2021","unstructured":"Huang, H. et al. Algorithm for detection and quantification of hyperreflective dots on optical coherence tomography in diabetic macular edema. Front. Med. 8, 688986 (2021).","journal-title":"Front. Med."},{"key":"1988_CR10","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.neucom.2018.05.080","volume":"311","author":"S Bai","year":"2018","unstructured":"Bai, S. & An, S. A survey on automatic image caption generation. Neurocomputing 311, 291\u2013304 (2018).","journal-title":"Neurocomputing"},{"key":"1988_CR11","doi-asserted-by":"publisher","first-page":"4019","DOI":"10.1007\/s10462-022-10270-w","volume":"56","author":"D-R Beddiar","year":"2023","unstructured":"Beddiar, D.-R., Oussalah, M. & Sepp\u00e4nen, T. Automatic captioning for medical imaging (mic): a rapid review of literature. Artif. Intell. Rev. 56, 4019\u20134076 (2023).","journal-title":"Artif. Intell. Rev."},{"key":"1988_CR12","doi-asserted-by":"crossref","unstructured":"Tian, J., Li, C., Shi, Z. & Xu, F. A diagnostic report generator from CT volumes on liver tumor with semi-supervised attention mechanism. In Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, (eds. Frangi, A., Schnabel, J., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G.) 11071 (Springer, 2018).","DOI":"10.1007\/978-3-030-00934-2_78"},{"key":"1988_CR13","doi-asserted-by":"crossref","unstructured":"Han, Z., Wei, B., Leung, S., Chung, J. & Li, S. Towards automatic report generation in spine radiology using weakly supervised framework. In Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, (eds. Frangi, A., Schnabel, J., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G.) 11073 (Springer, 2018).","DOI":"10.1007\/978-3-030-00937-3_22"},{"key":"1988_CR14","unstructured":"Liu, G. et al. Clinically accurate chest x-ray report generation. In Machine Learning for Healthcare Conference, 249\u2013269 (PMLR, 2019)."},{"key":"1988_CR15","doi-asserted-by":"crossref","unstructured":"Huang, J.-H. et al. Deepopht: Medical report generation for retinal images via deep models and visual explanation. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 2442\u20132452 (IEEE, 2021).","DOI":"10.1109\/WACV48630.2021.00249"},{"key":"1988_CR16","doi-asserted-by":"crossref","unstructured":"Huang, J.-H. et al. Non-local attention improves description generation for retinal images. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 1606\u20131615 (2022).","DOI":"10.1109\/WACV51458.2022.00331"},{"key":"1988_CR17","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Toshev, A., Bengio, S. & Erhan, D. Show and tell: a neural image caption generator. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3156\u20133164 (2015).","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"1988_CR18","doi-asserted-by":"publisher","unstructured":"Nooralahzadeh, F., Gonzalez, N. P., Frauenfelder, T., Fujimoto, K. & Krauthammer, M. Progressive transformer-based generation of radiology reports. arXiv https:\/\/doi.org\/10.48550\/arXiv.2102.09777 (2021).","DOI":"10.48550\/arXiv.2102.09777"},{"key":"1988_CR19","doi-asserted-by":"crossref","unstructured":"Chen, L. et al. Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5659\u20135667 (IEEE, 2017).","DOI":"10.1109\/CVPR.2017.667"},{"key":"1988_CR20","doi-asserted-by":"crossref","unstructured":"Anderson, P. et al. Bottom-up and top-down attention for image captioning and visual question answering. In Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, 6077\u20136086 (IEEE, 2018).","DOI":"10.1109\/CVPR.2018.00636"},{"key":"1988_CR21","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","volume":"622","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156\u2013163 (2023).","journal-title":"Nature"},{"key":"1988_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B. & Fu, Y. Residual dense network for image super-resolution. In Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, 2472\u20132481 (IEEE, 2018).","DOI":"10.1109\/CVPR.2018.00262"},{"key":"1988_CR23","doi-asserted-by":"publisher","unstructured":"Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv https:\/\/doi.org\/10.48550\/arXiv.1409.1556 (2014).","DOI":"10.48550\/arXiv.1409.1556"},{"key":"1988_CR24","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S-H Gao","year":"2019","unstructured":"Gao, S.-H. et al. Res2net: A new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43, 652\u2013662 (2019).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1988_CR25","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, 7132\u20137141 (IEEE, 2018).","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1988_CR26","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, 4700\u20134708 (2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"1988_CR27","doi-asserted-by":"crossref","unstructured":"d\u2019Ascoli, S. et al. Convit: Improving vision transformers with soft convolutional inductive biases. In International Conference on Machine Learning (PMLR, 2021).","DOI":"10.1088\/1742-5468\/ac9830"},{"key":"1988_CR28","doi-asserted-by":"crossref","unstructured":"Wang B. et al. Bridging the cross-modality semantic gap in visual question answering. In IEEE Transactions on Neural Networks and Learning Systems, 4519\u22124531 (IEEE, 2024).","DOI":"10.1109\/TNNLS.2024.3370925"},{"key":"1988_CR29","doi-asserted-by":"publisher","first-page":"3359","DOI":"10.1109\/TIP.2022.3170728","volume":"31","author":"J Song","year":"2022","unstructured":"Song, J., Chen, Y., Ye, J. & Song, M. Spot-sdaptive knowledge distillation. IEEE Trans. Image Process. 31, 3359\u20133370 (2022).","journal-title":"IEEE Trans. Image Process."},{"key":"1988_CR30","first-page":"2057","volume":"2048","author":"K Xu","year":"2015","unstructured":"Xu, K. et al. Show, attend and tell: neural image caption generation with visual attention. Comput. Sci. 2048, 2057 (2015).","journal-title":"Comput. Sci."},{"key":"1988_CR31","doi-asserted-by":"publisher","unstructured":"Dai, H. et al. Auggpt: Leveraging chatgpt for text data augmentation. arXiv https:\/\/doi.org\/10.48550\/arXiv.2302.13007 (2023).","DOI":"10.48550\/arXiv.2302.13007"},{"key":"1988_CR32","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhao, Z., Ouyang, X., Liu, T., Wang, Q. & Shen, D. Interactive computer-aided diagnosis on medical image using large language models. Commun Eng. 3, 133 (2024).","DOI":"10.1038\/s44172-024-00271-8"},{"key":"1988_CR33","doi-asserted-by":"crossref","unstructured":"Antaki, F., Chopra, R. & Keane, P. A. Vision-language models for feature detection of macular diseases on optical coherence tomography. JAMA Ophthalmol. 142, 573\u2212576 (2024).","DOI":"10.1001\/jamaophthalmol.2024.1165"},{"key":"1988_CR34","doi-asserted-by":"publisher","unstructured":"Zhu, D., Chen, J., Shen, X., Li, X. & Elhoseiny, M. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv https:\/\/doi.org\/10.48550\/arXiv.2304.10592 (2023).","DOI":"10.48550\/arXiv.2304.10592"},{"key":"1988_CR35","doi-asserted-by":"publisher","unstructured":"Hu, E. J. et al. Lora: Low-rank adaptation of large language models. arXiv https:\/\/doi.org\/10.48550\/arXiv.2106.09685 (2021).","DOI":"10.48550\/arXiv.2106.09685"},{"key":"1988_CR36","unstructured":"Houlsby, N. et al. Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning, 2790\u22122799 (PMLR, 2019)."},{"key":"1988_CR37","unstructured":"Rebuffi, S.-A., Bilen, H. & Vedaldi, A. Learning multiple visual domains with residual adapters. In Advances in neural Information Processing Systems 30, 506\u2212516 (2017)."},{"key":"1988_CR38","doi-asserted-by":"crossref","unstructured":"Li, X. L. & Liang, P. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021).","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"1988_CR39","doi-asserted-by":"publisher","unstructured":"Lester, B., Al-Rfou, R. & Constant, N. The power of scale for parameter-efficient prompt tuning. arXiv https:\/\/doi.org\/10.48550\/arXiv.2104.08691 (2021).","DOI":"10.48550\/arXiv.2104.08691"},{"key":"1988_CR40","doi-asserted-by":"publisher","unstructured":"Hambardzumyan, K., Khachatrian, H. & May, J. Warp: Word-level adversarial reprogramming. arXiv https:\/\/doi.org\/10.48550\/arXiv.2101.00190 (2021).","DOI":"10.48550\/arXiv.2101.00190"},{"key":"1988_CR41","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.aiopen.2023.08.012","volume":"5","author":"X Liu","year":"2024","unstructured":"Liu, X. et al. GPT understands, too. AI Open 5, 208\u2013215 (2024).","journal-title":"AI Open"},{"key":"1988_CR42","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s12938-023-01113-y","volume":"22","author":"T Pang","year":"2023","unstructured":"Pang, T., Li, P. & Zhao, L. A survey on automatic generation of medical imaging reports based on deep learning. Biomed. Eng. OnLine 22, 48 (2023).","journal-title":"Biomed. Eng. OnLine"},{"key":"1988_CR43","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-42444-7","volume":"14","author":"M Wang","year":"2023","unstructured":"Wang, M. et al. Uncertainty-inspired open set learning for retinal anomaly identification. Nat. Commun. 14, 6757 (2023).","journal-title":"Nat. Commun."},{"key":"1988_CR44","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R. S. & Vasconcelos, N. A unified multi-scale deep convolutional neural network for fast object detection. In European Conference on Computer Vision, 354\u2013370 (Springer, 2016).","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"1988_CR45","first-page":"1266","volume":"48","author":"Y Qunli","year":"2019","unstructured":"Qunli, Y., Xian, H. & Hong, L. Aircraft detection in remote sensing imagery with multi-scale feature fusion convolutional neural networks. Acta Geod. et. Cartogr. Sin. 48, 1266 (2019).","journal-title":"Acta Geod. et. Cartogr. Sin."},{"key":"1988_CR46","doi-asserted-by":"crossref","unstructured":"Qian, X., Fu, Y., Jiang, Y.-G., Xiang, T. & Xue, X. Multi-scale deep learning architectures for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision, 5399\u20135408 (IEEE, 2017).","DOI":"10.1109\/ICCV.2017.577"},{"key":"1988_CR47","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TMI.2021.3112716","volume":"41","author":"M Wang","year":"2021","unstructured":"Wang, M. et al. Mstganet: Automatic drusen segmentation from retinal oct images. IEEE Trans. Med. Imaging 41, 394\u2013406 (2021).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1988_CR48","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T. & Zhu, W.-J. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, 311\u2013318 (IEEE, 2002).","DOI":"10.3115\/1073083.1073135"},{"key":"1988_CR49","unstructured":"Lin, C.-Y. Rouge: A package for automatic evaluation of summaries. In Text Summarization Branches Iut, 74\u201381 (2004)."},{"key":"1988_CR50","doi-asserted-by":"crossref","unstructured":"Vedantam, R., Lawrence Zitnick, C. & Parikh, D. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4566\u20134575 (IEEE, 2015).","DOI":"10.1109\/CVPR.2015.7299087"},{"key":"1988_CR51","unstructured":"Over, P. & Yen, J. Intrinsic Evaluation Of Generic News Text Summarization Systems. https:\/\/www.slideserve.com\/oya\/introduction-to-duc-2002-an-intrinsic-evaluation-of-generic-news-text-summarization-systems (2003)."},{"key":"1988_CR52","doi-asserted-by":"publisher","unstructured":"White, J. et al. A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv https:\/\/doi.org\/10.48550\/arXiv.2302.11382 (2023).","DOI":"10.48550\/arXiv.2302.11382"},{"key":"1988_CR53","doi-asserted-by":"publisher","unstructured":"Wang, J. et al. Prompt engineering for healthcare: Methodologies and applications. arXiv https:\/\/doi.org\/10.48550\/arXiv.2304.14670 (2023).","DOI":"10.48550\/arXiv.2304.14670"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01988-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01988-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01988-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:05:02Z","timestamp":1761127502000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01988-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,20]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1988"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01988-2","relation":{},"ISSN":["2398-6352"],"issn-type":[{"type":"electronic","value":"2398-6352"}],"subject":[],"published":{"date-parts":[[2025,10,20]]},"assertion":[{"value":"24 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"618"}}