{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T15:29:18Z","timestamp":1788190158598,"version":"build-2803163510"},"reference-count":63,"publisher":"IOP Publishing","issue":"12","license":[{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.62177047"],"award-info":[{"award-number":["No.62177047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,6,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective<\/jats:italic>. Accurately and in real-time simulating soft tissue deformation and visualizing stress distribution are crucial for advancing surgical simulators closer to real surgical environments. The concept of using neural networks to accelerate the finite element method has emerged as a powerful approach for real-time physical modeling of soft tissues due to its excellent performance. However, existing models primarily focus on deformation modeling, neglecting the important guiding role of soft tissue stress field modeling in surgical training. Moreover, when modeling multiple physical fields, the vast differences in data distribution between these fields can cause a model to become biased toward features with larger scales if they are simply concatenated and fed into the network for training. This paper aims to address the issue of missing stress rendering in surgical simulators by developing a neural network-based real-time multi-physics modeling framework for soft tissues. <jats:italic>Approach<\/jats:italic>. By compactly encoding the nonlinear relationship between soft tissue boundary conditions and physical fields, the method accelerates the computation of deformation and stress fields. The feature scales of the physical fields are balanced using <jats:italic>Z<\/jats:italic>-Score normalization, which mitigates the problem of large-scale features dominating the model training. <jats:italic>Main results<\/jats:italic>. We validated the effectiveness of our method on three-dimensional models of a cantilever beam, liver, spleen, and kidney. Experiments demonstrate that our method achieves an excellent balance between efficiency and accuracy. Compared to traditional methods, it offers a 1000-fold or even 10\u2009000-fold improvement in efficiency with only around a 1% loss in accuracy. <jats:italic>Significance<\/jats:italic>. The proposed model effectively predicts the displacement and stress distribution of soft tissue, offering the potential to enhance surgical simulators with the capability to render multiple physical properties.<\/jats:p>","DOI":"10.1088\/1361-6560\/adde0d","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T22:55:40Z","timestamp":1748472940000},"page":"125007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Real-time integrated modeling of soft tissue deformation and stress based on deep learning"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1354-8262","authenticated-orcid":true,"given":"Ziyang","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8872-2117","authenticated-orcid":true,"given":"Shenghui","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Kui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renzhong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuyang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"pmbadde0dbib1","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.suc.2010.04.017","article-title":"Liver anatomy","volume":"90","author":"Abdel-Misih","year":"2010","journal-title":"Surg. Clin. North America"},{"key":"pmbadde0dbib2","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/S1369-7021(11)70059-1","article-title":"Characterizing the elastic properties of tissues","volume":"14","author":"Akhtar","year":"2011","journal-title":"Mater. Today"},{"key":"pmbadde0dbib3","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1002\/nme.2760","article-title":"Adjoint-weighted variational formulation for the direct solution of inverse problems of general linear elasticity with full interior data","volume":"81","author":"Barbone","year":"2010","journal-title":"Int. J. Numer. Methods Eng."},{"key":"pmbadde0dbib4","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1145\/212094.212141","article-title":"The computation of optical flow","volume":"27","author":"Beauchemin","year":"1995","journal-title":"ACM Comput. Surv."},{"key":"pmbadde0dbib5","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/0020-7683(88)90039-X","article-title":"Finite element simulation of indentation experiments","volume":"24","author":"Bhattacharya","year":"1988","journal-title":"Int. J. Solids Struct."},{"key":"pmbadde0dbib6","doi-asserted-by":"publisher","first-page":"3801","DOI":"10.1109\/LRA.2021.3063967","article-title":"Multi-sensory guidance and feedback for simulation-based training in robot assisted surgery: a preliminary comparison of visual, haptic and visuo-haptic","volume":"6","author":"Caccianiga","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"pmbadde0dbib7","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2102721118","article-title":"Learning hidden elasticity with deep neural networks","volume":"118","author":"Chen","year":"2021","journal-title":"Proc. Natl Acad. Sci."},{"key":"pmbadde0dbib8","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202300439","article-title":"Physics-informed deep-learning for elasticity: forward, inverse and mixed problems","volume":"10","author":"Chen","year":"2023","journal-title":"Adv. Sci."},{"key":"pmbadde0dbib9","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107324","article-title":"\u03b4-PINNs: physics-informed neural networks on complex geometries","volume":"127","author":"Costabal","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"pmbadde0dbib10","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/S1361-8415(02)00060-9","article-title":"Serial registration of intraoperative MR images of the brain","volume":"6","author":"Ferrant","year":"2002","journal-title":"Med. Image Anal."},{"key":"pmbadde0dbib11","doi-asserted-by":"publisher","DOI":"10.3791\/54872","article-title":"Biomechanical characterization of human soft tissues using indentation and tensile testing","volume":"118","author":"Griffin","year":"2016","journal-title":"J. Vis. Exp."},{"key":"pmbadde0dbib12","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad9a4b","article-title":"Simulating atherosclerotic plaque mechanics using polyvinyl alcohol (PVA) cryogel artery phantoms, ultrasound imaging and inverse finite element analysis","volume":"69","author":"Guendouz","year":"2024","journal-title":"Phys. Med. Biol."},{"key":"pmbadde0dbib13","first-page":"pp 433","article-title":"Tensor-mass model based real-time simulation of vessel deformation and force feedback for the interventional surgery training system","author":"Guo","year":"2017"},{"key":"pmbadde0dbib14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmbbm.2020.103798","article-title":"Estimation of the hyperelastic parameters of fresh human oropharyngeal soft tissues using indentation testing","volume":"108","author":"Haddad","year":"2020","journal-title":"J. Mech. Behav. Biomed. Mater."},{"key":"pmbadde0dbib15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105828","article-title":"Constitutive model characterization and discovery using physics-informed deep learning","volume":"120","author":"Haghighat","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"pmbadde0dbib16","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1109\/TMI.2018.2879495","article-title":"Data-driven elasticity imaging using Cartesian neural network constitutive models and the autoprogressive method","volume":"38","author":"Hoerig","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbadde0dbib17","doi-asserted-by":"publisher","first-page":"3026","DOI":"10.1088\/0031-9155\/61\/8\/3026","article-title":"A comparison of direct and iterative finite element inversion techniques in dynamic elastography","volume":"61","author":"Honarvar","year":"2016","journal-title":"Phys. Med. Biol."},{"key":"pmbadde0dbib18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmbbm.2024.106542","article-title":"Elastic parameter identification of three-dimensional soft tissue based on deep neural network","volume":"155","author":"Hu","year":"2024","journal-title":"J. Mech. Behav. Biomed. Mater."},{"key":"pmbadde0dbib19","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1016\/j.media.2014.07.001","article-title":"Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration","volume":"18","author":"Ji","year":"2014","journal-title":"Med. Image Anal."},{"key":"pmbadde0dbib20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TOH.2021.3137936","article-title":"Data-driven haptic texture modeling and rendering based on deep spatio-temporal networks","volume":"15","author":"Joolee","year":"2021","journal-title":"IEEE Trans. Haptics"},{"key":"pmbadde0dbib21","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/42.500139","article-title":"Tissue elasticity reconstruction using linear perturbation method","volume":"15","author":"Kallel","year":"1996","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbadde0dbib22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmbbm.2023.106228","article-title":"Physics-informed UNets for discovering hidden elasticity in heterogeneous materials","volume":"150","author":"Kamali","year":"2024","journal-title":"J. Mech. Behav. Biomed. Mater."},{"key":"pmbadde0dbib23","doi-asserted-by":"publisher","first-page":"3053","DOI":"10.1117\/1.1602089","article-title":"Evaluation of Young\u2019s modulus of a vibrating beam by optical method","volume":"42","author":"Kang","year":"2003","journal-title":"Opt. Eng., Bellingham"},{"key":"pmbadde0dbib24","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-informed machine learning","volume":"3","author":"Karniadakis","year":"2021","journal-title":"Nat. Rev. Phys."},{"key":"pmbadde0dbib25","doi-asserted-by":"publisher","DOI":"10.1038\/srep15538","article-title":"Quantitative micro-elastography: imaging of tissue elasticity using compression optical coherence elastography","volume":"5","author":"Kennedy","year":"2015","journal-title":"Sci. Rep."},{"key":"pmbadde0dbib26","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"pmbadde0dbib27","first-page":"pp 92","article-title":"Optical assessment of tissue mechanical properties","volume":"vol 4001","author":"Kirkpatrick","year":"2000"},{"key":"pmbadde0dbib28","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ada19d","article-title":"A comparative study of experimental and simulated ultrasound beam propagation through cranial bones","volume":"70","author":"Krokhmal","year":"2025","journal-title":"Phys. Med. Biol."},{"key":"pmbadde0dbib29","first-page":"pp 156","article-title":"Haptic simulation system for liver surgery based on variable virtual stiffness optimization","author":"Li","year":"2021"},{"key":"pmbadde0dbib30","article-title":"Fourier neural operator for parametric partial differential equations","author":"Li","year":"2021"},{"key":"pmbadde0dbib31","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2017.0844","article-title":"A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis","volume":"15","author":"Liang","year":"2018","journal-title":"J. R. Soc. Interface"},{"key":"pmbadde0dbib32","first-page":"pp 1387","article-title":"Real-time intra-operative 3D tissue deformation recovery","author":"Lo","year":"2008"},{"key":"pmbadde0dbib33","first-page":"pp 611","article-title":"Regularization by adversarial learning for ultrasound elasticity imaging","author":"Mohammadi","year":"2021"},{"key":"pmbadde0dbib34","first-page":"pp 742","article-title":"Real-time nonlinear FEM with neural network for simulating soft organ model deformation","volume":"vol 11","author":"Morooka","year":"2008"},{"key":"pmbadde0dbib35","first-page":"pp 194","article-title":"A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks","volume":"vol 8316","author":"Morooka","year":"2012"},{"key":"pmbadde0dbib36","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1097\/00006123-200104000-00019","article-title":"Serial intraoperative magnetic resonance imaging of brain shift","volume":"48","author":"Nabavi","year":"2001","journal-title":"Neurosurgery"},{"key":"pmbadde0dbib37","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1557\/s43577-020-00006-y","article-title":"A deep learning approach to the inverse problem of modulus identification in elasticity","volume":"46","author":"Ni","year":"2021","journal-title":"MRS Bull."},{"key":"pmbadde0dbib38","doi-asserted-by":"publisher","first-page":"2955","DOI":"10.1088\/0031-9155\/49\/13\/013","article-title":"Evaluation of the adjoint equation based algorithm for elasticity imaging","volume":"49","author":"Oberai","year":"2004","journal-title":"Phys. Med. Biol."},{"key":"pmbadde0dbib39","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1088\/0266-5611\/19\/2\/304","article-title":"Solution of inverse problems in elasticity imaging using the adjoint method","volume":"19","author":"Oberai","year":"2003","journal-title":"Inverse Problems"},{"key":"pmbadde0dbib40","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.cma.2019.04.045","article-title":"Circumventing the solution of inverse problems in mechanics through deep learning: Application to elasticity imaging","volume":"353","author":"Patel","year":"2019","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"pmbadde0dbib41","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113083","article-title":"Real-time biomechanical modeling of the liver using machine learning models trained on finite element method simulations","volume":"143","author":"Pellicer-Valero","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"pmbadde0dbib42","article-title":"Learning mesh-based simulation with graph networks","author":"Pfaff","year":"2021"},{"key":"pmbadde0dbib43","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1002\/mp.14602","article-title":"Real-time biomechanics using the finite element method and machine learning: review and perspective","volume":"48","author":"Phellan","year":"2021","journal-title":"Med. Phys."},{"key":"pmbadde0dbib44","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"Raissi","year":"2019","journal-title":"J. Comput. Phys."},{"key":"pmbadde0dbib45","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103221","article-title":"Comparing regularized Kelvinlet functions and the finite element method for registration of medical images to sparse organ data","volume":"96","author":"Ringel","year":"2024","journal-title":"Med. Image Anal."},{"key":"pmbadde0dbib46","first-page":"pp 234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"pmbadde0dbib47","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.neunet.2023.03.014","article-title":"Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity","volume":"162","author":"Roy","year":"2023","journal-title":"Neural Netw."},{"key":"pmbadde0dbib48","first-page":"pp 053","article-title":"Real-time haptic simulation of soft tissue deformation","author":"Sadraei","year":"2014"},{"key":"pmbadde0dbib49","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1109\/84.623107","article-title":"A new technique for measuring the mechanical properties of thin films","volume":"6","author":"Sharpe","year":"1997","journal-title":"J. Microelectromech. Syst."},{"key":"pmbadde0dbib50","doi-asserted-by":"publisher","DOI":"10.1016\/j.biomaterials.2022.121540","article-title":"Deep learning identification of stiffness markers in breast cancer","volume":"285","author":"Sneider","year":"2022","journal-title":"Biomaterials"},{"key":"pmbadde0dbib51","first-page":"pp 278","article-title":"A mass spring model for physically-realistic haptic rendering of deformable objects","author":"Suryawanshi","year":"2022"},{"key":"pmbadde0dbib52","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2024.103009","article-title":"Simulation of the selective laser sintering\/melting process of bioactive glass 45s5","volume":"136","author":"Svyetlichnyy","year":"2024","journal-title":"Simul. Model. Pract. Theory"},{"key":"pmbadde0dbib53","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102367","article-title":"An improved method for soft tissue modeling","volume":"65","author":"Tang","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"pmbadde0dbib54","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acc923","article-title":"Characterisation of the soft tissue viscous and elastic properties using ultrasound elastography and rheological models: validation and applications in plantar soft tissue assessment","volume":"68","author":"Tecse","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"pmbadde0dbib55","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.image.2018.12.002","article-title":"A survey of variational and cnn-based optical flow techniques","volume":"72","author":"Tu","year":"2019","journal-title":"Signal Process., Image Commun."},{"key":"pmbadde0dbib56","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1109\/TOH.2021.3077619","article-title":"Survey of pseudo-haptics: haptic feedback design and application proposals","volume":"14","author":"Ujitoko","year":"2021","journal-title":"IEEE Trans. Haptics"},{"key":"pmbadde0dbib57","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.jbiomech.2015.06.011","article-title":"Experimental and finite element analysis for prediction of kidney injury under blunt impact","volume":"52","author":"Umale","year":"2017","journal-title":"J. Biomech."},{"key":"pmbadde0dbib58","first-page":"pp 1655","article-title":"Filling model based soft tissue deformation model","author":"Ye","year":"2018"},{"key":"pmbadde0dbib59","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103659","article-title":"Formative feedback generation in a VR-based dental surgical skill training simulator","volume":"114","author":"Yin","year":"2021","journal-title":"J. Biomed. Inf."},{"key":"pmbadde0dbib60","first-page":"pp 1","article-title":"Displacement-based reconstruction of elasticity distribution with deep neural network","author":"Zhang","year":"2022"},{"key":"pmbadde0dbib61","doi-asserted-by":"publisher","DOI":"10.1016\/j.medntd.2022.100134","article-title":"Optical coherence elastography of 3D bilayer soft solids using full-field and partial displacement measurements","volume":"15","author":"Zhao","year":"2022","journal-title":"Med. Novel Technol. Devices"},{"key":"pmbadde0dbib62","first-page":"pp 771","article-title":"A real-time deformation modeling scheme of soft tissue for virtual surgical","author":"Zhu","year":"2010"},{"key":"pmbadde0dbib63","first-page":"pp 275","article-title":"Real-time biomechanically-based muscle volume deformation using FEM","volume":"vol 17","author":"Zhu","year":"1998"}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adde0d","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adde0d\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adde0d","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adde0d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adde0d\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adde0d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adde0d\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adde0d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T10:09:44Z","timestamp":1749722984000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adde0d"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,12]]},"references-count":63,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6,12]]},"published-print":{"date-parts":[[2025,6,22]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/adde0d","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,12]]},"assertion":[{"value":"Real-time integrated modeling of soft tissue deformation and stress based on deep learning","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-03-20","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-05-28","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-06-12","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}