Enhancing Precision in Window to the Brain Modeling: Methodology and Implementation of Hybrid Digital Twins
[EN] The Window to the Brain (WttB) is a novel cranial implant designed to enhance therapeutic procedures involving brain tissue. Previous computational models studying the effectiveness of the WttB exhibited some discrepancies with experimental results and inconsistencies in certain parameter value...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::3868e2c2cc5fd13581f791bcbe22efb2 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/234917 |
| Access Level: | acceso embargado |
| Palabra clave: | Hybrid digital twins Particle swarm optimization Grammatical evolution algorithm Mathematical computer modeling Photo-thermal laser effect Window to the brain |
| Sumario: | [EN] The Window to the Brain (WttB) is a novel cranial implant designed to enhance therapeutic procedures involving brain tissue. Previous computational models studying the effectiveness of the WttB exhibited some discrepancies with experimental results and inconsistencies in certain parameter values. To overcome these drawbacks, the following steps are followed. We first perform a domain reduction where the model is solved via the finite element method. Then, model parameters are calibrated using asynchronous random Particle Swarm Optimization (arPSO) algorithm. A statistical identifiability analysis is performed to evaluate how accurately model parameters are estimated based on the quantity and quality of experimental data. Afterward, we implement Hybrid Digital Twins (HDT) using Grammatical Evolution and Lexicase Selection to improve the model fitting keeping the model complexity. The outcomes demonstrate a complete alignment between experimental and computational results, as well as reasonable values for all model parameters. The final optimized model achieved a mean absolute error of 0.1871, with a standard deviation of 0.0013 and a 95% confidence interval (CI) of [0.1866, 0.1876], indicating a very low residual error and high stability across simulations. Our computational approach enhances the results from previous studies, which can be more useful for improving clinical practice. |
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