{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T04:50:56Z","timestamp":1768971056657,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,13]],"date-time":"2025-04-13T00:00:00Z","timestamp":1744502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>The design of orthopedic implants is a complex challenge, requiring the careful balance of mechanical performance and biological integration to ensure long-term success. This study focuses on the development of a porous femoral stem implant aimed at reducing stiffness and mitigating stress shielding effects. To accelerate the design process, neural networks were trained to predict the optimal density distribution of the implant, enabling rapid optimization. Two initial design spaces were evaluated, revealing the necessity of incorporating the femur\u2019s anatomical features into the design process. The trained models achieved a median error near 0 for both conventional and extended design spaces, producing optimized designs in a fraction of the computational time typically required. Finite element analysis (FEA) was employed to assess the mechanical performance of the neural network-generated implants. The results demonstrated that the neural network predictions effectively reduced stress shielding compared to a solid model in 50% of the test cases. While the graded porosity implant design did not show significant differences in stress shielding prevention compared to a uniform porosity design, it was found to be significantly stronger, highlighting its potential for enhanced durability. This work underscores the efficacy of neural network-accelerated design in improving implant development efficiency and performance.<\/jats:p>","DOI":"10.3390\/biomimetics10040238","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T04:42:07Z","timestamp":1744605727000},"page":"238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Neural Network-Accelerated Approach for Orthopedic Implant Design and Evaluation Through Strain Shielding Analysis"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3462-2102","authenticated-orcid":false,"given":"Ana Isabel Lopes","family":"Pais","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"},{"name":"INEGI\u2014Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9327-9092","authenticated-orcid":false,"given":"Jorge","family":"Lino Alves","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"},{"name":"INEGI\u2014Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0539-7057","authenticated-orcid":false,"given":"Jorge","family":"Belinha","sequence":"additional","affiliation":[{"name":"INEGI\u2014Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 400, 4200-465 Porto, Portugal"},{"name":"ISEP, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida, n. 431, 4249-015 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wolff, J. 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