{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:09:49Z","timestamp":1779365389239,"version":"3.53.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"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":["Vis Comput"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00371-026-04470-6","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T18:43:20Z","timestamp":1777056200000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing medical image segmentation with the modification of U-shaped network"],"prefix":"10.1007","volume":"42","author":[{"given":"Yiwei","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiren","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maksim","family":"Davydov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serestina","family":"Viriri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irsa","family":"Talib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangguang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"4470_CR1","first-page":"234","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, pp. 234\u2013241. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich (2015)"},{"issue":"12","key":"4470_CR2","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4470_CR3","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"4470_CR4","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, SA.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"4470_CR5","first-page":"424","volume-title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., et al.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation, pp. 424\u2013432. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Greece (2016)"},{"key":"4470_CR6","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz, N., Rahman, M.S.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74\u201387 (2020)","journal-title":"Neural Netw."},{"key":"4470_CR7","doi-asserted-by":"crossref","unstructured":"Chen, LC., Zhu, Y., Papandreou, G., et al.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision (ECCV), Munich, Germany, pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"4470_CR8","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman\u00a0Siddiquee, MM., Tajbakhsh, N., et al.: UNet++: a nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain, pp. 3\u201311 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"4470_CR9","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, P., Wang, J., et al.: UCTransNet: rethinking the skip connections in U-Net from a channel-wise perspective with transformer. In: AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, pp. 2441\u20132449 (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"4470_CR10","first-page":"304","volume-title":"Attention U-Net: Learning Where to Look for the Pancreas","author":"O Oktay","year":"2018","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., et al.: Attention U-Net: Learning Where to Look for the Pancreas, pp. 304\u2013312. Medical Imaging with Deep Learning, London (2018)"},{"issue":"11","key":"4470_CR11","first-page":"8259","volume":"18","author":"T Pei","year":"2022","unstructured":"Pei, T., Wu, Z., Zhang, W.: Boundary-aware segmentation network for mobile and web applications. IEEE Trans. Ind. Inf. 18(11), 8259\u20138269 (2022)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"4470_CR12","doi-asserted-by":"crossref","unstructured":"Takikawa, T., Acuna, D., Jampani, V., et al.: Gated-SCNN: gated shape CNNs for semantic segmentation. In: IEEE International Conference on Computer Vision (ICCV), Seoul, South Korea, pp. 5229\u20135238 (2019)","DOI":"10.1109\/ICCV.2019.00533"},{"key":"4470_CR13","first-page":"285","volume-title":"Boundary Loss for Highly Unbalanced Segmentation","author":"H Kervadec","year":"2019","unstructured":"Kervadec, H., Bouchtiba, J., Desrosiers, C., et al.: Boundary Loss for Highly Unbalanced Segmentation, pp. 285\u2013296. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Shenzhen (2019)"},{"issue":"2","key":"4470_CR14","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2020","unstructured":"Karimi, D., Salcudean, S.E.: Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans. Med. Imaging 39(2), 499\u2013513 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"4470_CR15","first-page":"1","volume":"40","author":"Y Fan","year":"2024","unstructured":"Fan, Y., Song, J., Yuan, L., et al.: HCT-Unet: multi-target medical image segmentation via a hybrid CNN-transformer Unet incorporating multi-axis gated multi-layer perceptron. Vis. Comput. 40(1), 1\u201316 (2024)","journal-title":"Vis. Comput."},{"issue":"6","key":"4470_CR16","doi-asserted-by":"publisher","first-page":"4319","DOI":"10.1007\/s00371-023-03084-6","volume":"40","author":"W Liu","year":"2024","unstructured":"Liu, W., Li, Y., Huang, D.: RA-UNet: an improved network model for image denoising. Vis. Comput. 40(6), 4319\u20134335 (2024)","journal-title":"Vis. Comput."},{"issue":"10","key":"4470_CR17","doi-asserted-by":"publisher","first-page":"4801","DOI":"10.1007\/s00371-022-02628-6","volume":"39","author":"X Wang","year":"2023","unstructured":"Wang, X., Hua, Z., Li, J.: Cross-UNet: dual-branch infrared and visible image fusion framework based on cross-convolution and attention mechanism. Vis. Comput. 39(10), 4801\u20134818 (2023)","journal-title":"Vis. Comput."},{"key":"4470_CR18","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, LC.: MobileNetV2: Inverted residuals and linear bottlenecks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 2018, pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"4470_CR19","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., et al.: UNet 3+: a full-scale connected UNet for medical image segmentation. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"4470_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113233","volume":"314","author":"T Hussain","year":"2025","unstructured":"Hussain, T., Shouno, H., Mohammed, M.A., Marhoon, H.A., Alam, T.: DCSSGA-UNet: biomedical image segmentation with DenseNet channel spatial and semantic guidance attention. Knowl. Based Syst. 314, 113233 (2025)","journal-title":"Knowl. Based Syst."},{"key":"4470_CR21","doi-asserted-by":"publisher","first-page":"54040","DOI":"10.1109\/ACCESS.2025.3554184","volume":"13","author":"T Hussain","year":"2025","unstructured":"Hussain, T., Shouno, H., Hussain, A., Hussain, D., Ismail, M., Mir, T.H.: EFFResNet-ViT: a fusion-based convolutional and vision transformer model for explainable medical image classification. IEEE Access 13, 54040\u201354068 (2025)","journal-title":"IEEE Access"},{"key":"4470_CR22","doi-asserted-by":"crossref","unstructured":"Li, G., Li, S., Lin, Y., et al.: CFSeg-Net: context feature extraction network for medical image segmentation. Vis. Comput. 41, 10205\u201310215 (2025)","DOI":"10.1007\/s00371-025-04029-x"},{"key":"4470_CR23","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4470_CR24","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, JY., et al.: CBAM: convolutional block attention module. In: European Conference on Computer Vision (ECCV), Munich, Germany, pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"4470_CR25","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., et al.: Residual attention network for image classification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 3156\u20133164 (2017)","DOI":"10.1109\/CVPR.2017.683"},{"key":"4470_CR26","doi-asserted-by":"publisher","first-page":"1398237","DOI":"10.3389\/fbioe.2024.1398237","volume":"12","author":"G Sun","year":"2024","unstructured":"Sun, G., Pan, Y., Kong, W., Xu, Z., Ma, J., Racharak, T., Nguyen, L.M., Xin, J.: DA-TransUNet: integrating spatial and channel dual attention with transformer U-Net for medical image segmentation. Front. Bioeng. Biotechnol. 12, 1398237 (2024)","journal-title":"Front. Bioeng. Biotechnol."},{"key":"4470_CR27","doi-asserted-by":"publisher","first-page":"33687","DOI":"10.1109\/ACCESS.2024.3372394","volume":"12","author":"G Sun","year":"2024","unstructured":"Sun, G., Shu, H., Shao, F., Racharak, T., Kong, W., Pan, Y., Dong, J., Wang, S., Nguyen, L.M., Xin, J.: FKD-Med: privacy-aware, communication-optimized medical image segmentation via federated learning and model lightweighting through knowledge distillation. IEEE Access 12, 33687\u201333704 (2024)","journal-title":"IEEE Access"},{"key":"4470_CR28","doi-asserted-by":"crossref","unstructured":"Chen, X., Williams, BM., Vallabhaneni, SR., et al.: Learning active contour models for medical image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 11632\u201311640 (2019)","DOI":"10.1109\/CVPR.2019.01190"},{"key":"4470_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Xu, M., Bai, S., et al.: Asymmetric non-local neural networks for semantic segmentation. In: IEEE International Conference on Computer Vision (ICCV), Seoul, South Korea, pp. 593\u2013602 (2019)","DOI":"10.1109\/ICCV.2019.00068"},{"key":"4470_CR30","doi-asserted-by":"crossref","unstructured":"Wang, H., Xie, S., Lin, L., et al.: Mixed transformer U-Net for medical image segmentation. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, pp. 2390\u20132394 (2022)","DOI":"10.1109\/ICASSP43922.2022.9746172"},{"key":"4470_CR31","doi-asserted-by":"publisher","first-page":"1504249","DOI":"10.3389\/fbioe.2024.1504249","volume":"12","author":"Y Pan","year":"2024","unstructured":"Pan, Y., Xin, J., Yang, T., Racharak, T., Nguyen, L.M., Sun, G.: A mutual inclusion mechanism for precise boundary segmentation in medical images. Front. Bioeng. Biotechnol. 12, 1504249 (2024)","journal-title":"Front. Bioeng. Biotechnol."},{"key":"4470_CR32","doi-asserted-by":"publisher","first-page":"1727075","DOI":"10.3389\/fpls.2025.1727075","volume":"16","author":"G Sun","year":"2025","unstructured":"Sun, G., Li, T., Pan, Y., Zhu, Z., Yang, T., Shao, F., Guo, J., Xin, J.: Gradient-guided boundary-aware selective scanning with multi-scale context aggregation for plant lesion segmentation. Front. Plant Sci. 16, 1727075 (2025)","journal-title":"Front. Plant Sci."},{"key":"4470_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., et al.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"issue":"6","key":"4470_CR34","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"J Canny","year":"1986","unstructured":"Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679\u2013698 (1986)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4470_CR35","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)","journal-title":"Sci. Data"},{"key":"4470_CR36","doi-asserted-by":"crossref","unstructured":"Codella, N.C.F., Gutman, D., Celebi ME.: Skin lesion analysis toward melanoma detection: a challenge at the, et al.: International symposium on biomedical imaging. In: IEEE International Symposium on Biomedical Imaging, Washington, DC, USA, 2018, pp. 168\u2013172 (2017)","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"4470_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., et al.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)","journal-title":"Data Brief"},{"issue":"3","key":"4470_CR38","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1002\/mp.14676","volume":"48","author":"J Ma","year":"2021","unstructured":"Ma, J., Wang, Y., An, X., et al.: Towards data-efficient learning: a benchmark for COVID-19 CT lung and infection segmentation. Med. Phys. 48(3), 1197\u20131210 (2021)","journal-title":"Med. Phys."},{"key":"4470_CR39","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Ferreira, PM., Marques, JS., et al.: PH2\u2014a dermoscopic image database for research and benchmarking. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, pp. 5437\u20135440 (2013)","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"4470_CR40","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., et al.: Swin-UNet: UNet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision (ECCV), Glasgow, UK, pp. 205\u2013218 (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"4470_CR41","doi-asserted-by":"crossref","unstructured":"Wang, Z., Min, X., Shi, F., et al.: SMESwin Unet: Merging CNN and transformer for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Strasbourg, France, pp. 517\u2013526 (2022)","DOI":"10.1007\/978-3-031-16443-9_50"},{"key":"4470_CR42","doi-asserted-by":"crossref","unstructured":"Valanarasu, JMJ., Patel, VM.: UNext: MLP-based rapid medical image segmentation network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Singapore, pp. 23\u201333 (2022)","DOI":"10.1007\/978-3-031-16443-9_3"},{"key":"4470_CR43","doi-asserted-by":"crossref","unstructured":"Ibtehaz, N., Kihara, D.: ACC-UNet: A completely convolutional unet model for the 2020s. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Strasbourg, France, pp. 692\u2013702 (2023)","DOI":"10.1007\/978-3-031-43898-1_66"},{"key":"4470_CR44","unstructured":"Yeung, M., Sala, E., Sch\u00f6nlieb, C.B., Rundo, L.: U-Lite: a lightweight and efficient U-Net architecture for medical image segmentation. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1813\u20131820. Taipei, Taiwan (2023)"},{"key":"4470_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110313","volume":"150","author":"B Chen","year":"2024","unstructured":"Chen, B., Liu, Y., Zhang, Z., et al.: GA-Net: generative attention network for multi-organ segmentation. Pattern Recogn. 150, 110313 (2024)","journal-title":"Pattern Recogn."},{"key":"4470_CR46","first-page":"2529","volume":"33","author":"Z Liu","year":"2024","unstructured":"Liu, Z., Wang, L., Li, Z., et al.: Multi-scale dense feature aggregation network for medical image segmentation. IEEE Trans. Image Process. 33, 2529\u20132542 (2024)","journal-title":"IEEE Trans. Image Process."},{"key":"4470_CR47","unstructured":"Sun, Y., Zhang, L., Wang, H., et al.: TinyU-Net: extremely lightweight U-Net for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Marrakesh, Morocco, pp. 157\u2013166 (2024)"},{"key":"4470_CR48","doi-asserted-by":"crossref","unstructured":"Wu, R., Liu, Y., Liang, P., Chang, Q.: H-VMUNet: High-order vision Mamba UNet for medical image segmentation. Neurocomputing 624, Art. no. 129447 (2025)","DOI":"10.1016\/j.neucom.2025.129447"},{"issue":"7","key":"4470_CR49","doi-asserted-by":"publisher","first-page":"6993","DOI":"10.1109\/TCSVT.2025.3542969","volume":"35","author":"G Yue","year":"2025","unstructured":"Yue, G., et al.: Boundary-guided feature-aligned network for colorectal polyp segmentation. IEEE Trans. Circuits Syst. Video Technol. 35(7), 6993\u20137004 (2025)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4470_CR50","doi-asserted-by":"publisher","unstructured":"Xu, Q., Li, J., He, X., Li, C., Tesema, F.B., Duan, W., Chen, Z., Qu, R., Garibaldi, J.M., Chen, C.W.: De-LightSAM: modality-decoupled lightweight SAM for generalizable medical segmentation. IEEE Trans. Circuits Syst. Video Technol. (2025). https:\/\/doi.org\/10.1109\/TCSVT.2025.3621309","DOI":"10.1109\/TCSVT.2025.3621309"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04470-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04470-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04470-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:44:10Z","timestamp":1779363850000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04470-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":50,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["4470"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04470-6","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"29 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"267"}}