{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T06:36:05Z","timestamp":1793255765253,"version":"4.5.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T00:00:00Z","timestamp":1748044800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T00:00:00Z","timestamp":1748044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100022242","name":"Botnar Research Centre for Child Health, University of Basel","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100022242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>This study investigates the effectiveness of synthetic training data in predicting 2D landmarks for 3D intraoral reconstruction in cleft lip and palate patients. We take inspiration from existing landmark prediction and 3D reconstruction techniques for faces and demonstrate their potential in medical applications.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We generated both real and synthetic datasets from intraoral scans and videos. A convolutional neural network was trained using a negative-Gaussian log-likelihood loss function to predict 2D landmarks and their corresponding confidence scores. The predicted landmarks were then used to fit a statistical shape model to generate 3D reconstructions from individual images. We analyzed the model\u2019s performance on real patient data and explored the dataset size required to overcome the domain gap between synthetic and real images.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Our approach generates satisfying results on synthetic data and shows promise when tested on real data. The method achieves rapid 3D reconstruction from single images and can therefore provide significant value in day-to-day medical work.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Our results demonstrate that synthetic training data are viable for training models to predict 2D landmarks and reconstruct 3D meshes in patients with cleft lip and palate. This approach offers an accessible, low-cost alternative to traditional methods, using smartphone technology for noninvasive, rapid, and accurate 3D reconstructions in clinical settings.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11548-025-03396-z","type":"journal-article","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T12:46:04Z","timestamp":1748090764000},"page":"1471-1479","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation of synthetic training data for 3D intraoral reconstruction of cleft patients from single images"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4308-979X","authenticated-orcid":false,"given":"Lasse","family":"Lingens","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoriko","family":"Lill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prasad","family":"Nalabothu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benito K.","family":"Benitez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas A.","family":"Mueller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Gross","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Solenthaler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,24]]},"reference":[{"key":"3396_CR1","doi-asserted-by":"publisher","unstructured":"Mossey PA, Modell B (2012) Epidemiology of Oral Clefts 2012: An international perspective. In: Cleft lip and palate: epidemiology, aetiology and treatment. S.Karger AG, Basel. https:\/\/doi.org\/10.1159\/000337464","DOI":"10.1159\/000337464"},{"key":"3396_CR2","unstructured":"Mishra B, Singh AK, Zaidi J, Singh GK, Agrawal R, Kumar V (2010) Presurgical nasoalveolar molding for correction of cleft lip nasal deformity: experience from northern india. Eplasty 10"},{"key":"3396_CR3","doi-asserted-by":"publisher","first-page":"53","DOI":"10.2147\/CCIDE.S129598","volume":"9","author":"I Alzain","year":"2017","unstructured":"Alzain I, Batwa W, Cash A, Murshid ZA (2017) Presurgical cleft lip and palate orthopedics: an overview. Clin Cosmet Investig Dent 9:53\u201359","journal-title":"Clin Cosmet Investig Dent"},{"issue":"3","key":"3396_CR4","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1097\/01.prs.0000270847.12427.25","volume":"120","author":"V Ko\u017eelj","year":"2007","unstructured":"Ko\u017eelj V (2007) Experience with presurgical nasal molding in infants with cleft lip and nose deformity. Plast Reconstr Surg 120(3):738\u2013745","journal-title":"Plast Reconstr Surg"},{"key":"3396_CR5","doi-asserted-by":"publisher","unstructured":"Schnabel TN, G\u00f6zc\u00fc B, Gotardo P, Lingens L, Dorda D, Vetterli F, Emhemmed A, Nalabothu P, Lill Y, Benitez BK, Mueller AA, Gross M, Solenthaler B (2023) Automated and data-driven plate computation for presurgical cleft lip and palate treatment. Int J Comput Assist Radiol Surg. https:\/\/doi.org\/10.1007\/s11548-023-02858-6","DOI":"10.1007\/s11548-023-02858-6"},{"issue":"4","key":"3396_CR6","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1179\/bjo.22.4.299","volume":"22","author":"RAC Chate","year":"1995","unstructured":"Chate RAC (1995) A report on the hazards encountered when taking neonatal cleft palate impressions (1983\u20131992). Br J Orthod 22(4):299\u2013307. https:\/\/doi.org\/10.1179\/bjo.22.4.299. (PMID: 8580095)","journal-title":"Br J Orthod"},{"issue":"3","key":"3396_CR7","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1093\/ejo\/cjab068","volume":"44","author":"C Weise","year":"2021","unstructured":"Weise C, Frank K, Wiechers C, Weise H, Reinert S, Koos B, Xepapadeas AB (2021) Intraoral scanning of neonates and infants with craniofacial disorders: feasibility, scanning duration, and clinical experience. Eur J Orthod 44(3):279\u2013286. https:\/\/doi.org\/10.1093\/ejo\/cjab068","journal-title":"Eur J Orthod"},{"key":"3396_CR8","doi-asserted-by":"crossref","unstructured":"Wood E, Baltru\u0161aitis T, Hewitt C, Johnson M, Shen J, Milosavljevi\u0107 N, Wilde D, Garbin S, Sharp T, Stojiljkovi\u0107 I (2022) 3d face reconstruction with dense landmarks. In: European conference on computer vision. Springer, Berlin, pp 160\u2013177","DOI":"10.1007\/978-3-031-19778-9_10"},{"key":"3396_CR9","doi-asserted-by":"publisher","unstructured":"Bansal A, Nanduri A, Castillo CD, Ranjan R, Chellappa R (2017) Umdfaces: An annotated face dataset for training deep networks. In: 2017 IEEE international joint conference on biometrics (IJCB), pp 464\u2013473. https:\/\/doi.org\/10.1109\/BTAS.2017.8272731","DOI":"10.1109\/BTAS.2017.8272731"},{"key":"3396_CR10","doi-asserted-by":"crossref","unstructured":"Wood E, Baltrusaitis T, Hewitt C, Dziadzio S, Johnson M, Estellers V, Cashman TJ, Shotton J (2021) Fake it till you make it: face analysis in the wild using synthetic data alone. CoRR abs\/2109.15102 arxiv:2109.15102","DOI":"10.1109\/ICCV48922.2021.00366"},{"key":"3396_CR11","doi-asserted-by":"publisher","unstructured":"Lingens L, G\u00f6zc\u00fc B, Schnabel T, Lill Y, Benitez BK, Nalabothu P, Mueller AA, Gross M, Solenthaler B (2023) Image-based 3d reconstruction of cleft lip and palate using a learned shape prior. In: Applications of medical artificial intelligence: second international workshop, AMAI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. Springer, Berlin, pp 94\u2013103. https:\/\/doi.org\/10.1007\/978-3-031-47076-9_10","DOI":"10.1007\/978-3-031-47076-9_10"},{"issue":"1","key":"3396_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s11263-007-0071-y","volume":"76","author":"A Saxena","year":"2008","unstructured":"Saxena A, Chung SH, Ng AY (2008) 3-d depth reconstruction from a single still image. Int J Comput Vis 76(1):53\u201369. https:\/\/doi.org\/10.1007\/s11263-007-0071-y","journal-title":"Int J Comput Vis"},{"key":"3396_CR13","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980233","author":"C Wu","year":"2016","unstructured":"Wu C, Bradley D, Garrido P, Zollh\u00f6fer M, Theobalt C, Gross M, Beeler T (2016) Model-based teeth reconstruction. ACM Trans Graph. https:\/\/doi.org\/10.1145\/2980179.2980233","journal-title":"ACM Trans Graph"},{"key":"3396_CR14","doi-asserted-by":"publisher","unstructured":"Blanz V, Vetter T (1999) A morphable model for the synthesis of 3d faces. In: Proceedings of the 26th annual conference on computer graphics and interactive techniques. SIGGRAPH \u201999, USA, pp 187\u2013194. https:\/\/doi.org\/10.1145\/311535.311556","DOI":"10.1145\/311535.311556"},{"key":"3396_CR15","doi-asserted-by":"publisher","unstructured":"Booth J, Roussos A, Zafeiriou S, Ponniah A, Dunaway D (2016) A 3d morphable model learnt from 10,000 faces. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 5543\u20135552. https:\/\/doi.org\/10.1109\/CVPR.2016.598","DOI":"10.1109\/CVPR.2016.598"},{"key":"3396_CR16","doi-asserted-by":"crossref","unstructured":"Zhang T, Chu X, Liu Y, Lin L, Yang Z, Xu Z, Cao C, Yu F, Zhou C, Yuan C, Li Y (2023) Accurate 3d face reconstruction with facial component tokens. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 9033\u20139042","DOI":"10.1109\/ICCV51070.2023.00829"},{"key":"3396_CR17","doi-asserted-by":"crossref","unstructured":"Park JJ, Florence PR, Straub J, Newcombe RA, Lovegrove S (2019) Deepsdf: learning continuous signed distance functions for shape representation. CoRR abs\/1901.05103arxiv:1901.05103","DOI":"10.1109\/CVPR.2019.00025"},{"key":"3396_CR18","doi-asserted-by":"crossref","unstructured":"Chabra R, Lenssen JE, Ilg E, Schmidt T, Straub J, Lovegrove S, Newcombe RA (2020) Deep local shapes: learning local SDF priors for detailed 3d reconstruction. CoRR abs\/2003.10983arxiv:2003.10983","DOI":"10.1007\/978-3-030-58526-6_36"},{"key":"3396_CR19","unstructured":"Genova K, Cole F, Sud A, Sarna A, Funkhouser TA (2019) Deep structured implicit functions. CoRR abs\/1912.06126arxiv:1912.06126"},{"key":"3396_CR20","doi-asserted-by":"publisher","unstructured":"Hewitt C, Saleh F, Aliakbarian S, Petikam L, Rezaeifar S, Florentin L, Hosenie Z, Cashman TJ, Valentin J, Cosker D, Baltrusaitis T (2024) Look ma, no markers: holistic performance capture without the hassle. ACM Trans Graph 43(6):1\u201312. https:\/\/doi.org\/10.1145\/3687772","DOI":"10.1145\/3687772"},{"key":"3396_CR21","unstructured":"Community BO (2018) Blender\u2014a 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam. Blender Foundation. http:\/\/www.blender.org"},{"key":"3396_CR22","doi-asserted-by":"crossref","unstructured":"Neoral M, \u0160er\u00fdch J, Matas J (2023) MFT: Long-term tracking of every pixel. arxiv:2305.12998","DOI":"10.1109\/WACV57701.2024.00669"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03396-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-025-03396-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03396-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T14:51:24Z","timestamp":1751554284000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-025-03396-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,24]]},"references-count":22,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["3396"],"URL":"https:\/\/doi.org\/10.1007\/s11548-025-03396-z","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,24]]},"assertion":[{"value":"6 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The collection and further use of the clinical data received the IRB approval by Ethikkommission Nordwest- und Zentralschweiz (EKNZ) on March 5, 2020 (protocol number 2020-00388) and was registered on  (identifier NCT04342234).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The parents have given their consent for the use of the image material presented in the figures.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}