{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T09:25:49Z","timestamp":1787995549082,"version":"build-2784847793"},"reference-count":39,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR\u201010\u2010IAHU\u201001"],"award-info":[{"award-number":["ANR\u201010\u2010IAHU\u201001"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR\u201021\u2010PMRB\u20100004"],"award-info":[{"award-number":["ANR\u201021\u2010PMRB\u20100004"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100023214","name":"Fondation Princesse Grace de Monaco","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023214","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["American J of Med Genetics Pt A"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Background Facial analysis tools can assist in diagnosing rare genetic syndromes, but their accuracy is limited in ultra\u2010rare conditions and underrepresented ethnicities due to small, biased datasets. Synthetic facial images could enrich training data and improve equity in diagnostic performance. Methods We developed a synthetic face generation pipeline using diffusion models (DreamShaper XL Turbo), enhanced with LoRA\u2010based syndrome\u2010specific domain adaptation and pose conditioning via ControlNet. A total of 4432 synthetic faces were generated across ten rare syndromes, balanced by age (0\u201318\u2009years), sex (50\/50), and ethnicity (33% Caucasian, Afro\u2010Caribbean, Asian). Synthetic and real data were used in four machine learning designs to train and test ArcFace R\u2010100, a phenotyping algorithm for syndromic classification. Results Synthetic faces generated from real patient data achieved high phenotypic realism, with classification performance reaching top\u20101 accuracy of 0.823 and AUC of 0.991. Adding synthetic images to real training datasets increased accuracy on real test images from 0.766 to 0.869 and improved AUC from 0.988 to 0.993. Performance gains were most significant for ultra\u2010rare syndromes and Asian individuals (top\u20101: 0.971). Training with synthetic images alone yielded lower accuracy (top\u20101: 0.606), underscoring their complementary role. Conclusion This study demonstrates that diffusion\u2010generated synthetic faces can enhance inclusivity and accuracy in AI\u2010based dysmorphology tools. These synthetic datasets provide a scalable, ethical solution to data scarcity, with applications in clinical training and telemedicine, especially in underserved regions.<\/jats:p>","DOI":"10.1002\/ajmg.a.64298","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T06:30:07Z","timestamp":1762410607000},"page":"685-694","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evaluation of a New Inclusive Next\u2010Generation Synthetic Face Tool for Dysmorphology"],"prefix":"10.1002","volume":"200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7926-1366","authenticated-orcid":false,"given":"Ludovic","family":"Benichou","sequence":"first","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Craniofacial Growth and Form, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Department of Maxillofacial Surgery Paris Saint\u2010Joseph Hospital Group  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luan","family":"Breton","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Craniofacial Growth and Form, Imagine Institute, INSERM UMR1163  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Garcelon","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Benichou","sequence":"additional","affiliation":[{"name":"Cabinet Wagram  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Lienhard","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeanne","family":"Amiel","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Department of Genomic Medicine for Rare Diseases, Necker\u2010Enfants Malades Hospital, Assistance Publique \u2010 H\u00f4pitaux de Paris; Faculty of Medicine Universit\u00e9 de Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Bongibault","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Craniofacial Growth and Form, Imagine Institute, INSERM UMR1163  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana\u2010Julia Bravo","family":"Hidalgo","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Department of Maxillofacial Surgery Paris Saint\u2010Joseph Hospital Group  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Val\u00e9rie","family":"Cormier\u2010Daire","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Department of Genomic Medicine for Rare Diseases, Necker\u2010Enfants Malades Hospital, Assistance Publique \u2010 H\u00f4pitaux de Paris; Faculty of Medicine Universit\u00e9 de Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanislas","family":"Lyonnet","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Department of Genomic Medicine for Rare Diseases, Necker\u2010Enfants Malades Hospital, Assistance Publique \u2010 H\u00f4pitaux de Paris; Faculty of Medicine Universit\u00e9 de Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnaud","family":"Picard","sequence":"additional","affiliation":[{"name":"Department of Maxillofacial Surgery and Plastic Surgery, Necker\u2010Enfants Malades Hospital, Assistance Publique \u2010 H\u00f4pitaux de Paris; Reference Center for Rare Facial and Oral Cavity Malformations MAFACE, Head and Neck Rare Diseases Network; Faculty of Medicine Universit\u00e9 de Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marl\u00e8ne","family":"Rio","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Department of Genomic Medicine for Rare Diseases, Necker\u2010Enfants Malades Hospital, Assistance Publique \u2010 H\u00f4pitaux de Paris; Faculty of Medicine Universit\u00e9 de Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Zaiter","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roman H.","family":"Khonsari","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Craniofacial Growth and Form, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Department of Maxillofacial Surgery and Plastic Surgery, Necker\u2010Enfants Malades Hospital, Assistance Publique \u2010 H\u00f4pitaux de Paris; Reference Center for Rare Facial and Oral Cavity Malformations MAFACE, Head and Neck Rare Diseases Network; Faculty of Medicine Universit\u00e9 de Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quentin","family":"Hennocq","sequence":"additional","affiliation":[{"name":"Data Science, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Craniofacial Growth and Form, Imagine Institute, INSERM UMR1163  Paris France"},{"name":"Department of Maxillofacial Surgery and Plastic Surgery, Necker\u2010Enfants Malades Hospital, Assistance Publique \u2010 H\u00f4pitaux de Paris; Reference Center for Rare Facial and Oral Cavity Malformations MAFACE, Head and Neck Rare Diseases Network; Faculty of Medicine Universit\u00e9 de Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"e_1_2_15_2_1","first-page":"1445","article-title":"Partial FC: Training 10 Million Identities on a Single Machine [Internet]","volume":"2021","author":"An X.","year":"2021","journal-title":"Proceeding of IEEE\/CVF International Conferences on Computer Vision Workshops"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/jmg.33.2.89"},{"key":"e_1_2_15_4_1","unstructured":"Chung H. 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