{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T15:45:50Z","timestamp":1768405550117,"version":"3.49.0"},"reference-count":29,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009480","name":"Stichting Vrienden van het Sophia","doi-asserted-by":"publisher","award":["WAR23\u201049"],"award-info":[{"award-number":["WAR23\u201049"]}],"id":[{"id":"10.13039\/501100009480","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Anatomy"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Craniosynostosis, a congenital condition characterized by the premature fusion of cranial sutures, necessitates objective methods for evaluating cranial morphology to enhance patient treatment. Current subjective assessments often lead to inconsistent outcomes. This study introduces a novel, quantitative approach to classify craniosynostosis and measure its severity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>An artificial neural network was trained to classify normocephalic, trigonocephalic, and scaphocephalic head shapes based on a publicly available dataset of synthetic 3D head models. Each 3D model was converted into a low\u2010dimensional shape representation based on the distribution of normal vectors, which served as the input for the neural network, ensuring complete patient anonymity and invariance to geometric size and orientation. Explainable AI methods were utilized to highlight significant features when making predictions. Additionally, the Feature Prominence (FP) score was introduced, a novel metric that captures the prominence of distinct shape characteristics associated with a given class. Its relationship with clinical severity scores was examined using the Spearman Rank Correlation Coefficient.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The final model achieved excellent test accuracy in classifying the different cranial shapes from their low\u2010dimensional representation. Attention maps indicated that the network's attention was predominantly directed toward the parietal and temporal regions, as well as toward the region signifying vertex depression in scaphocephaly. In trigonocephaly, features around the temples were most pronounced. The FP score showed a strong positive monotonic relationship with clinical severity scores in both scaphocephalic (<jats:italic>\u03c1<\/jats:italic>\u2009=\u20090.83, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) and trigonocephalic (<jats:italic>\u03c1<\/jats:italic>\u2009=\u20090.64, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) models. Visual assessments further confirmed that as FP values rose, phenotypic severity became increasingly evident.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study presents an innovative and accessible AI\u2010based method for quantifying cranial shape that mitigates the need for adjustments due to age\u2010specific size variations or differences in the spatial orientation of the 3D images, while ensuring complete patient privacy. The proposed FP score strongly correlates with clinical severity scores and has the potential to aid in clinical decision\u2010making and facilitate multi\u2010center collaborations. Future work will focus on validating the model with larger patient datasets and exploring the potential of the FP score for broader applications. The publicly available source code facilitates easy implementation, aiming to advance craniofacial care and research.<\/jats:p><\/jats:sec>","DOI":"10.1111\/joa.14061","type":"journal-article","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T05:44:21Z","timestamp":1716011061000},"page":"903-913","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Quantifying dysmorphologies of the neurocranium using artificial neural networks"],"prefix":"10.1111","volume":"245","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4215-4406","authenticated-orcid":false,"given":"Tareq","family":"Abdel\u2010Alim","sequence":"first","affiliation":[{"name":"Department of Neurosurgery Erasmus Medical Center  Rotterdam The Netherlands"},{"name":"Department of Radiology and Nuclear Medicine Erasmus Medical Center  Rotterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Franz","family":"Tapia\u00a0Chaca","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine Erasmus Medical Center  Rotterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irene M. J.","family":"Mathijssen","sequence":"additional","affiliation":[{"name":"Department of Plastic and Reconstructive Surgery Erasmus Medical Center  Rotterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Clemens M. F.","family":"Dirven","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Erasmus Medical Center  Rotterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wiro J.","family":"Niessen","sequence":"additional","affiliation":[{"name":"Faculty of Medical Sciences University of Groningen  Groningen The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eppo B.","family":"Wolvius","sequence":"additional","affiliation":[{"name":"Department of Oral\u2010 and Maxillofacial Surgery Erasmus Medical Center  Rotterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marie\u2010Lise C.","family":"van Veelen","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Erasmus Medical Center  Rotterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gennady V.","family":"Roshchupkin","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine Erasmus Medical Center  Rotterdam The Netherlands"},{"name":"Department of Epidemiology Erasmus Medical Center  Rotterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000007379"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"Amberg B. Romdhani S.&Vetter T.(2007)Optimal step nonrigid ICP algorithms for surface registration. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR.2007.383165"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/PRS.0000000000009853"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1597\/09-059.1"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000006215"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1159\/000533168"},{"key":"e_1_2_9_8_1","first-page":"10\u20101097","article-title":"Creation of the Scaphocephalic index: measurement of global and regional severity in scaphocephaly\u2019","author":"Bins G.P.","year":"2023","journal-title":"Plastic and Reconstructive Surgery"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-24756-7"},{"issue":"1","key":"e_1_2_9_10_1","first-page":"1","article-title":"Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis","volume":"10","author":"Jong G.","year":"2020","journal-title":"Scientific Reports"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107689"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000009732"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000009773"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000007369"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1061-3315(01)00003-8"},{"issue":"12","key":"e_1_2_9_16_1","first-page":"p2863","article-title":"Craniosynostosis","volume":"69","author":"Kabbani H.","year":"2004","journal-title":"American Family Physician"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1097\/GOX.0000000000002171"},{"key":"e_1_2_9_18_1","first-page":"226","volume-title":"Machine Learning for Health","author":"Mashouri P.","year":"2020"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000008620"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2023.3278030"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12071516"},{"key":"e_1_2_9_22_1","first-page":"446","volume-title":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom","author":"Schaufelberger M.","year":"2022"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118575574"},{"key":"e_1_2_9_24_1","first-page":"3319","volume-title":"International conference on machine learning","author":"Sundararajan M.","year":"2017"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79565-0_29"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2018.03.014"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7032"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1097\/PRS.0000000000003688"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0305-z"}],"container-title":["Journal of Anatomy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/joa.14061","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T18:28:48Z","timestamp":1731090528000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/joa.14061"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,17]]},"references-count":29,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10.1111\/joa.14061"],"URL":"https:\/\/doi.org\/10.1111\/joa.14061","archive":["Portico"],"relation":{},"ISSN":["0021-8782","1469-7580"],"issn-type":[{"value":"0021-8782","type":"print"},{"value":"1469-7580","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,17]]},"assertion":[{"value":"2023-12-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-30","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}