{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T10:38:38Z","timestamp":1787999918110,"version":"build-2784847793"},"reference-count":25,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003784","name":"Great Ormond Street Hospital for Children","doi-asserted-by":"publisher","award":["12SG15"],"award-info":[{"award-number":["12SG15"]}],"id":[{"id":"10.13039\/501100003784","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIHR Biomedical Research Centre Advanced Therapies for Structural Malformations and Tissue damage pump-prime funding call","award":["17DS18"],"award-info":[{"award-number":["17DS18"]}]},{"name":"Great Ormond Street Hospital Charity Clinical Research Starter Grant","award":["17DD46"],"award-info":[{"award-number":["17DD46"]}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["ERC-2017-StG- 757923"],"award-info":[{"award-number":["ERC-2017-StG- 757923"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Craniosynostosis is a medical condition that affects the growth of babies\u2019 heads, caused by an early fusion of cranial sutures. In recent decades, surgical treatments for craniosynostosis have significantly improved, leading to reduced invasiveness, faster recovery, and less blood loss. At Great Ormond Street Hospital (GOSH), the main surgical treatment for patients diagnosed with sagittal craniosynostosis (SC) is spring assisted cranioplasty (SAC). This procedure involves a\n                    <jats:inline-formula id=\"pone.0336473.e001\">\n                      <jats:alternatives>\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" id=\"pone.0336473.e001g\" mimetype=\"image\" position=\"anchor\" xlink:href=\"info:doi\/10.1371\/journal.pone.0336473.e001\" xlink:type=\"simple\"\/>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" id=\"M1\">\n                          <mml:mrow>\n                            <mml:mn>15<\/mml:mn>\n                            <mml:mo>\u00d7<\/mml:mo>\n                            <mml:mn>15<\/mml:mn>\n                            <mml:msup>\n                              <mml:mtext>mm<\/mml:mtext>\n                              <mml:mn>2<\/mml:mn>\n                            <\/mml:msup>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    osteotomy, where two springs are inserted to induce distraction. Despite the numerous advantages of this surgical technique for patients, the outcome remains unpredictable due to the lack of efficient preoperative planning tools. The surgeon\u2019s experience and the baby\u2019s age are currently relied upon to determine the osteotomy location and spring selection. Previous tools for predicting the surgical outcome of SC relied on finite element modeling (FEM), which involved computed tomography (CT) imaging and required engineering expertise and lengthy calculations. The main goal of this research is to develop a real-time prediction tool for the surgical outcome of patients, eliminating the need for CT scans to minimise radiation exposure during preoperative planning. The proposed methodology involves creating personalised synthetic skulls based on three-dimensional (3D) photographs, incorporating population average values of suture location, skull thickness, and soft tissue properties. A machine learning (ML) surrogate model is employed to achieve the desired surgical outcome. The resulting multi-output support vector regressor model achieves a\n                    <jats:inline-formula id=\"pone.0336473.e002\">\n                      <jats:alternatives>\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" id=\"pone.0336473.e002g\" mimetype=\"image\" position=\"anchor\" xlink:href=\"info:doi\/10.1371\/journal.pone.0336473.e002\" xlink:type=\"simple\"\/>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" id=\"M2\">\n                          <mml:mrow>\n                            <mml:msup>\n                              <mml:mtext>R<\/mml:mtext>\n                              <mml:mn>2<\/mml:mn>\n                            <\/mml:msup>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    metric of 0.95 and MSE and MAE below 0.13. Furthermore, in the future, this model could not only simulate various surgical scenarios but also provide optimal parameters for achieving a maximum cranial index (CI).\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0336473","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T19:05:44Z","timestamp":1764961544000},"page":"e0336473","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["A combined machine learning and finite element modelling tool for the surgical planning of craniosynostosis correction"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4575-2579","authenticated-orcid":true,"given":"Itxasne","family":"Ant\u00fanez S\u00e1enz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ane","family":"Alberdi 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