{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T23:57:02Z","timestamp":1789171022170,"version":"build-2803163510"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T00:00:00Z","timestamp":1706400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T00:00:00Z","timestamp":1706400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"crossref","award":["ANR-10-IAHU-01"],"award-info":[{"award-number":["ANR-10-IAHU-01"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100017170","name":"Thailand Science Research and Innovation","doi-asserted-by":"crossref","award":["N42A650229"],"award-info":[{"award-number":["N42A650229"]}],"id":[{"id":"10.13039\/501100017170","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, <jats:italic>KMT2D<\/jats:italic>-related) from KS2 (KS type 2, <jats:italic>KDM6A<\/jats:italic>-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the <jats:italic>KMT2D<\/jats:italic> gene (KS1) and 23 (22%) in the <jats:italic>KDM6A<\/jats:italic> gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9\u201399.9%, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729\u20130.880, p\u2009&lt;\u20090.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.<\/jats:p>","DOI":"10.1038\/s41598-024-52691-3","type":"journal-article","created":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T13:02:06Z","timestamp":1706446926000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Next generation phenotyping for diagnosis and phenotype\u2013genotype correlations in Kabuki syndrome"],"prefix":"10.1038","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7882-8287","authenticated-orcid":false,"given":"Quentin","family":"Hennocq","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marjolaine","family":"Willems","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeanne","family":"Amiel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"St\u00e9phanie","family":"Arpin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tania","family":"Attie-Bitach","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Bongibault","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Bouygues","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Val\u00e9rie","family":"Cormier-Daire","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Corre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Dieterich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxime","family":"Douillet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean","family":"Feydy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Galliani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabienne","family":"Giuliano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanislas","family":"Lyonnet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnaud","family":"Picard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thantrira","family":"Porntaveetus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marl\u00e8ne","family":"Rio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Flavien","family":"Rouxel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vorasuk","family":"Shotelersuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annick","family":"Toutain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Yauy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Genevi\u00e8ve","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roman H.","family":"Khonsari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Garcelon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,28]]},"reference":[{"key":"52691_CR1","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.3390\/genes13101761","volume":"13","author":"KK Barry","year":"2022","unstructured":"Barry, K. K. et al. From genotype to phenotype\u2014a review of Kabuki syndrome. Genes (Basel) 13, 1761 (2022).","journal-title":"Genes (Basel)"},{"key":"52691_CR2","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1002\/ajmg.1320310312","volume":"31","author":"N Niikawa","year":"1988","unstructured":"Niikawa, N. et al. Kabuki make-up (Niikawa-Kuroki) syndrome: A study of 62 patients. Am. J. Med. Genet. 31, 565\u2013589 (1988).","journal-title":"Am. J. Med. Genet."},{"key":"52691_CR3","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1002\/ajmg.a.20674","volume":"127A","author":"SM White","year":"2004","unstructured":"White, S. M. et al. Growth, behavior, and clinical findings in 27 patients with Kabuki (Niikawa-Kuroki) syndrome. Am. J. Med. Genet. A 127A, 118\u2013127 (2004).","journal-title":"Am. J. Med. Genet. A"},{"key":"52691_CR4","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1016\/S0022-3476(81)80256-9","volume":"99","author":"Y Kuroki","year":"1981","unstructured":"Kuroki, Y., Suzuki, Y., Chyo, H., Hata, A. & Matsui, I. A new malformation syndrome of long palpebral fissures, large ears, depressed nasal tip, and skeletal anomalies associated with postnatal dwarfism and mental retardation. J. Pediatr. 99, 570\u2013573 (1981).","journal-title":"J. Pediatr."},{"key":"52691_CR5","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1038\/s41431-021-00994-8","volume":"30","author":"F Rouxel","year":"2022","unstructured":"Rouxel, F. et al. Using deep-neural-network-driven facial recognition to identify distinct Kabuki syndrome 1 and 2 gestalt. Eur. J. Hum. Genet. 30, 682\u2013686 (2022).","journal-title":"Eur. J. Hum. Genet."},{"key":"52691_CR6","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1111\/j.1399-0004.2004.00348.x","volume":"67","author":"MP Adam","year":"2005","unstructured":"Adam, M. P. & Hudgins, L. Kabuki syndrome: A review. Clin. Genet. 67, 209\u2013219 (2005).","journal-title":"Clin. Genet."},{"key":"52691_CR7","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1002\/humu.23026","volume":"37","author":"N B\u00f6gershausen","year":"2016","unstructured":"B\u00f6gershausen, N. et al. Mutation update for Kabuki syndrome genes KMT2D and KDM6A and further delineation of X-Linked Kabuki syndrome subtype 2. Hum. Mutat. 37, 847\u2013864 (2016).","journal-title":"Hum. Mutat."},{"key":"52691_CR8","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.ajhg.2011.11.021","volume":"90","author":"D Lederer","year":"2012","unstructured":"Lederer, D. et al. Deletion of KDM6A, a Histone Demethylase Interacting with MLL2, in three patients with Kabuki Syndrome. Am. J. Hum. Genet. 90, 119\u2013124 (2012).","journal-title":"Am. J. Hum. Genet."},{"key":"52691_CR9","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1111\/cge.12754","volume":"90","author":"J Pad\u011brov\u00e1","year":"2016","unstructured":"Pad\u011brov\u00e1, J. et al. Molecular genetic analysis in 14 Czech Kabuki syndrome patients is confirming the utility of phenotypic scoring. Clin. Genet. 90, 230\u2013237 (2016).","journal-title":"Clin. Genet."},{"key":"52691_CR10","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1111\/cge.12363","volume":"87","author":"S Banka","year":"2015","unstructured":"Banka, S. et al. Novel KDM6A (UTX) mutations and a clinical and molecular review of the X-linked Kabuki syndrome (KS2). Clin. Genet. 87, 252\u2013258 (2015).","journal-title":"Clin. Genet."},{"key":"52691_CR11","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1111\/cge.12051","volume":"83","author":"N B\u00f6gershausen","year":"2013","unstructured":"B\u00f6gershausen, N. & Wollnik, B. Unmasking Kabuki syndrome. Clin. Genet. 83, 201\u2013211 (2013).","journal-title":"Clin. Genet."},{"key":"52691_CR12","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1038\/ng.646","volume":"42","author":"SB Ng","year":"2010","unstructured":"Ng, S. B. et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat. Genet. 42, 790\u2013793 (2010).","journal-title":"Nat. Genet."},{"key":"52691_CR13","unstructured":"DGOS. Les maladies rares. Minist\u00e8re de la Sant\u00e9 et de la Pr\u00e9vention https:\/\/sante.gouv.fr\/soins-et-maladies\/prises-en-charge-specialisees\/maladies-rares\/article\/les-maladies-rares (2023)."},{"key":"52691_CR14","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","volume":"380","author":"A Rajkomar","year":"2019","unstructured":"Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347\u20131358 (2019).","journal-title":"N. Engl. J. Med."},{"key":"52691_CR15","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1148\/radiol.2018171820","volume":"288","author":"G Choy","year":"2018","unstructured":"Choy, G. et al. Current applications and future impact of machine learning in radiology. Radiology 288, 318\u2013328 (2018).","journal-title":"Radiology"},{"key":"52691_CR16","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1001\/jamadermatol.2019.1633","volume":"155","author":"RA Novoa","year":"2019","unstructured":"Novoa, R. A., Gevaert, O. & Ko, J. M. Marking the path toward artificial intelligence-based image classification in dermatology. JAMA Dermatol. 155, 1105\u20131106 (2019).","journal-title":"JAMA Dermatol."},{"key":"52691_CR17","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1001\/jamasurg.2019.4917","volume":"155","author":"TJ Loftus","year":"2020","unstructured":"Loftus, T. J. et al. Artificial Intelligence and surgical decision-making. JAMA Surg. 155, 148\u2013158 (2020).","journal-title":"JAMA Surg."},{"key":"52691_CR18","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1038\/s41591-018-0279-0","volume":"25","author":"Y Gurovich","year":"2019","unstructured":"Gurovich, Y. et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat. Med. 25, 60\u201364 (2019).","journal-title":"Nat. Med."},{"key":"52691_CR19","doi-asserted-by":"publisher","first-page":"808919","DOI":"10.3389\/fgene.2022.808919","volume":"13","author":"Q Zhang","year":"2022","unstructured":"Zhang, Q. et al. Molecular and phenotypic expansion of alstr\u00f6m syndrome in Chinese patients. Front. Genet. 13, 808919 (2022).","journal-title":"Front. Genet."},{"key":"52691_CR20","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1038\/s41433-021-01563-5","volume":"36","author":"MJ Javitt","year":"2022","unstructured":"Javitt, M. J., Vanner, E. A., Grajewski, A. L. & Chang, T. C. Evaluation of a computer-based facial dysmorphology analysis algorithm (Face2Gene) using standardized textbook photos. Eye 36, 859\u2013861 (2022).","journal-title":"Eye"},{"key":"52691_CR21","doi-asserted-by":"publisher","first-page":"E1042","DOI":"10.3390\/ijms21031042","volume":"21","author":"A Latorre-Pellicer","year":"2020","unstructured":"Latorre-Pellicer, A. et al. Evaluating Face2Gene as a tool to identify cornelia de lange syndrome by facial phenotypes. Int. J. Mol. Sci. 21, E1042 (2020).","journal-title":"Int. J. Mol. Sci."},{"key":"52691_CR22","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1038\/s10038-019-0619-z","volume":"64","author":"H Mishima","year":"2019","unstructured":"Mishima, H. et al. Evaluation of Face2Gene using facial images of patients with congenital dysmorphic syndromes recruited in Japan. J. Hum. Genet. 64, 789\u2013794 (2019).","journal-title":"J. Hum. Genet."},{"key":"52691_CR23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s12896-017-0410-1","volume":"17","author":"T Dudding-Byth","year":"2017","unstructured":"Dudding-Byth, T. et al. Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability. BMC Biotechnol. 17, 90 (2017).","journal-title":"BMC Biotechnol."},{"key":"52691_CR24","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.jbi.2018.02.019","volume":"80","author":"N Garcelon","year":"2018","unstructured":"Garcelon, N. et al. A clinician friendly data warehouse oriented toward narrative reports: Dr Warehouse. J. Biomed. Inform. 80, 52\u201363 (2018).","journal-title":"J. Biomed. Inform."},{"key":"52691_CR25","doi-asserted-by":"publisher","unstructured":"Koonce, B. ResNet 50 63\u201372 (Springer, 2021). https:\/\/doi.org\/10.1007\/978-1-4842-6168-2_6.","DOI":"10.1007\/978-1-4842-6168-2_6"},{"key":"52691_CR26","doi-asserted-by":"publisher","unstructured":"Paszke, A. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. https:\/\/doi.org\/10.48550\/arXiv.1912.01703 (2019).","DOI":"10.48550\/arXiv.1912.01703"},{"key":"52691_CR27","doi-asserted-by":"publisher","unstructured":"Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. https:\/\/doi.org\/10.48550\/arXiv.1412.6980 (2017).","DOI":"10.48550\/arXiv.1412.6980"},{"key":"52691_CR28","doi-asserted-by":"publisher","unstructured":"Alabort-i-Medina, J., Antonakos, E., Booth, J., Snape, P. & Zafeiriou, S. Menpo: A comprehensive platform for parametric image alignment and visual deformable models. In Proceedings of the 22nd ACM international conference on Multimedia 679\u2013682 (ACM, 2014). https:\/\/doi.org\/10.1145\/2647868.2654890.","DOI":"10.1145\/2647868.2654890"},{"key":"52691_CR29","first-page":"145","volume":"2022","author":"Q Hennocq","year":"2022","unstructured":"Hennocq, Q. et al. An automatic facial landmarking for children with rare diseases. Am. J. Med. Genet. Part A 2022, 145 (2022).","journal-title":"Am. J. Med. Genet. Part A"},{"key":"52691_CR30","volume-title":"An Iterative Image Registration Technique with an Application to Stereo Vision (IJCAI)","author":"B Lucas","year":"1981","unstructured":"Lucas, B. & Kanade, T. An Iterative Image Registration Technique with an Application to Stereo Vision (IJCAI) Vol. 81 (Springer, 1981)."},{"key":"52691_CR31","unstructured":"landmarker.io. The Menpo Project. https:\/\/www.menpo.org\/landmarkerio\/ (2022)."},{"key":"52691_CR32","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2466\/pr0.1966.19.1.3","volume":"19","author":"JJ Bartko","year":"1966","unstructured":"Bartko, J. J. The intraclass correlation coefficient as a measure of reliability. Psychol. Rep. 19, 3\u201311 (1966).","journal-title":"Psychol. Rep."},{"key":"52691_CR33","doi-asserted-by":"publisher","first-page":"40","DOI":"10.2307\/2992207","volume":"39","author":"FJ Rohlf","year":"1990","unstructured":"Rohlf, F. J. & Slice, D. Extensions of the procrustes method for the optimal superimposition of landmarks. Syst. Zool. 39, 40\u201359 (1990).","journal-title":"Syst. Zool."},{"key":"52691_CR34","doi-asserted-by":"publisher","first-page":"2355","DOI":"10.1111\/2041-210X.13723","volume":"12","author":"EK Baken","year":"2021","unstructured":"Baken, E. K., Collyer, M. L., Kaliontzopoulou, A. & Adams, D. C. geomorph v4.0 and gmShiny: Enhanced analytics and a new graphical interface for a comprehensive morphometric experience. Methods Ecol. Evol. 12, 2355\u20132363 (2021).","journal-title":"Methods Ecol. Evol."},{"key":"52691_CR35","first-page":"348","volume":"13","author":"H Avc\u0131","year":"2023","unstructured":"Avc\u0131, H. & Karakaya, J. A novel medical image enhancement algorithm for breast cancer detection on mammography images using machine learning. Diagn. (Basel) 13, 348 (2023).","journal-title":"Diagn. (Basel)"},{"key":"52691_CR36","doi-asserted-by":"publisher","first-page":"18","DOI":"10.2174\/1874120701307010018","volume":"7","author":"L Anifah","year":"2013","unstructured":"Anifah, L., Purnama, I. K. E., Hariadi, M. & Purnomo, M. H. Osteoarthritis classification using self organizing map based on Gabor Kernel and contrast-limited adaptive histogram equalization. Open Biomed. Eng. J. 7, 18 (2013).","journal-title":"Open Biomed. Eng. J."},{"key":"52691_CR37","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.aej.2020.06.054","volume":"60","author":"C Huang","year":"2021","unstructured":"Huang, C., Li, X. & Wen, Y. AN OTSU image segmentation based on fruitfly optimization algorithm. Alexandr. Eng. J. 60, 183\u2013188 (2021).","journal-title":"Alexandr. Eng. J."},{"key":"52691_CR38","doi-asserted-by":"publisher","first-page":"1032467","DOI":"10.3389\/fpubh.2022.1032467","volume":"10","author":"A Kiflie","year":"2023","unstructured":"Kiflie, A., Tesema Tufa, G. & Salau, A. O. Sputum smears quality inspection using an ensemble feature extraction approach. Front. Public Health 10, 1032467 (2023).","journal-title":"Front. Public Health"},{"key":"52691_CR39","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"3","author":"RM Haralick","year":"1973","unstructured":"Haralick, R. M., Shanmugam, K. & Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610\u2013621 (1973).","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"52691_CR40","volume-title":"Image Texture Feature Extraction Using GLCM Approach","author":"P Mohanaiah","year":"2013","unstructured":"Mohanaiah, P., Sathyanarayana, P. & GuruKumar, L. Image Texture Feature Extraction Using GLCM Approach Vol. 3 (Springer, 2013)."},{"key":"52691_CR41","doi-asserted-by":"publisher","first-page":"e0212110","DOI":"10.1371\/journal.pone.0212110","volume":"14","author":"T L\u00f6fstedt","year":"2019","unstructured":"L\u00f6fstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T. & Garpebring, A. Gray-level invariant Haralick texture features. PLoS One 14, e0212110 (2019).","journal-title":"PLoS One"},{"key":"52691_CR42","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1186\/s12880-023-01058-7","volume":"23","author":"P Mundt","year":"2023","unstructured":"Mundt, P. et al. Periaortic adipose radiomics texture features associated with increased coronary calcium score-first results on a photon-counting-CT. BMC Med. Imaging 23, 97 (2023).","journal-title":"BMC Med. Imaging"},{"key":"52691_CR43","doi-asserted-by":"publisher","first-page":"110931","DOI":"10.1016\/j.ejrad.2023.110931","volume":"165","author":"G Adelsmayr","year":"2023","unstructured":"Adelsmayr, G. et al. Three dimensional computed tomography texture analysis of pulmonary lesions: Does radiomics allow differentiation between carcinoma, neuroendocrine tumor and organizing pneumonia?. Eur. J. Radiol. 165, 110931 (2023).","journal-title":"Eur. J. Radiol."},{"key":"52691_CR44","doi-asserted-by":"publisher","first-page":"1131816","DOI":"10.3389\/fonc.2023.1131816","volume":"13","author":"B Peng","year":"2023","unstructured":"Peng, B. et al. Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer. Front. Oncol. 13, 1131816 (2023).","journal-title":"Front. Oncol."},{"key":"52691_CR45","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ijpharm.2013.10.024","volume":"460","author":"X Ou","year":"2014","unstructured":"Ou, X., Pan, W. & Xiao, P. In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). Int. J. Pharm. 460, 28\u201332 (2014).","journal-title":"Int. J. Pharm."},{"key":"52691_CR46","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1038\/gim.2016.3","volume":"18","author":"M Muenke","year":"2016","unstructured":"Muenke, M., Adeyemo, A. & Kruszka, P. An electronic atlas of human malformation syndromes in diverse populations. Genet. Med. 18, 1085\u20131087 (2016).","journal-title":"Genet. Med."},{"key":"52691_CR47","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1056\/NEJMsb025007","volume":"348","author":"EG Burchard","year":"2003","unstructured":"Burchard, E. G. et al. The importance of race and ethnic background in biomedical research and clinical practice. N. Engl. J. Med. 348, 1170\u20131175 (2003).","journal-title":"N. Engl. J. Med."},{"key":"52691_CR48","doi-asserted-by":"publisher","unstructured":"Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785\u2013794 (Association for Computing Machinery, 2016). https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"52691_CR49","doi-asserted-by":"publisher","first-page":"2","DOI":"10.18637\/jss.v079.c02","volume":"79","author":"MC Sachs","year":"2017","unstructured":"Sachs, M. C. plotROC: A tool for plotting ROC curves. J. Stat. Softw. 79, 2 (2017).","journal-title":"J. Stat. Softw."},{"key":"52691_CR50","doi-asserted-by":"publisher","unstructured":"McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. https:\/\/doi.org\/10.48550\/arXiv.1802.03426 (2020).","DOI":"10.48550\/arXiv.1802.03426"},{"key":"52691_CR51","unstructured":"R Core Team. European Environment Agency. https:\/\/www.eea.europa.eu\/data-and-maps\/indicators\/oxygen-consuming-substances-in-rivers\/r-development-core-team-2006 (2020)."},{"key":"52691_CR52","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1136\/adc.70.6.512","volume":"70","author":"HE Hughes","year":"1994","unstructured":"Hughes, H. E. & Davies, S. J. Coarctation of the aorta in Kabuki syndrome. Arch. Dis. Child. 70, 512\u2013514 (1994).","journal-title":"Arch. Dis. Child."},{"key":"52691_CR53","doi-asserted-by":"publisher","first-page":"2912","DOI":"10.1002\/ajmg.a.38417","volume":"173","author":"MC Digilio","year":"2017","unstructured":"Digilio, M. C. et al. Congenital heart defects in molecularly proven Kabuki syndrome patients. Am. J. Med. Genet. A 173, 2912\u20132922 (2017).","journal-title":"Am. J. Med. Genet. A"},{"key":"52691_CR54","doi-asserted-by":"publisher","first-page":"317","DOI":"10.3345\/kjp.2015.58.9.317","volume":"58","author":"C-K Cheon","year":"2015","unstructured":"Cheon, C.-K. & Ko, J. M. Kabuki syndrome: Clinical and molecular characteristics. Korean J. Pediatr. 58, 317\u2013324 (2015).","journal-title":"Korean J. Pediatr."},{"key":"52691_CR55","doi-asserted-by":"publisher","first-page":"256","DOI":"10.3345\/kjp.2015.58.7.256","volume":"58","author":"JK Yoon","year":"2015","unstructured":"Yoon, J. K. et al. The strong association of left-side heart anomalies with Kabuki syndrome. Korean J. Pediatr. 58, 256\u2013262 (2015).","journal-title":"Korean J. Pediatr."},{"key":"52691_CR56","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1038\/s41436-021-01119-8","volume":"23","author":"V Faundes","year":"2021","unstructured":"Faundes, V. et al. Clinical delineation, sex differences, and genotype-phenotype correlation in pathogenic KDM6A variants causing X-linked Kabuki syndrome type 2. Genet. Med. 23, 1202\u20131210 (2021).","journal-title":"Genet. Med."},{"key":"52691_CR57","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/s41436-018-0013-9","volume":"21","author":"KL Yap","year":"2019","unstructured":"Yap, K. L. et al. Congenital hyperinsulinism as the presenting feature of Kabuki syndrome: Clinical and molecular characterization of 9 affected individuals. Genet. Med. 21, 233\u2013242 (2019).","journal-title":"Genet. Med."},{"key":"52691_CR58","doi-asserted-by":"publisher","first-page":"848","DOI":"10.4081\/cp.2016.848","volume":"6","author":"H Gole","year":"2016","unstructured":"Gole, H., Chuk, R. & Coman, D. Persistent hyperinsulinism in Kabuki syndrome 2: Case report and literature review. Clin. Pract. 6, 848 (2016).","journal-title":"Clin. Pract."},{"key":"52691_CR59","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1159\/000488347","volume":"89","author":"CE Gibson","year":"2018","unstructured":"Gibson, C. E. et al. Congenital hyperinsulinism in infants with turner syndrome: Possible association with monosomy X and KDM6A haploinsufficiency. Horm. Res. Paediatr. 89, 413\u2013422 (2018).","journal-title":"Horm. Res. Paediatr."},{"key":"52691_CR60","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.jpeds.2013.02.032","volume":"163","author":"J-B Courcet","year":"2013","unstructured":"Courcet, J.-B. et al. Clinical and molecular spectrum of renal malformations in Kabuki syndrome. J. Pediatr. 163, 742\u2013746 (2013).","journal-title":"J. Pediatr."},{"key":"52691_CR61","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1515\/JPEM.2001.14.6.803","volume":"14","author":"E Cetinkaya","year":"2001","unstructured":"Cetinkaya, E., Misirlio\u011flu, E. D., Vidinlisan, S., Baydar, Z. & Ozhan, Z. R. Hypospadias in a patient with Kabuki make-up (Niikawa-Kuroki) syndrome. J. Pediatr. Endocrinol. Metab. 14, 803\u2013805 (2001).","journal-title":"J. Pediatr. Endocrinol. Metab."},{"key":"52691_CR62","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1002\/ajmg.a.63155","volume":"191","author":"SS Shah","year":"2023","unstructured":"Shah, S. S. et al. Insights into the genotype-phenotype relationship of ocular manifestations in Kabuki syndrome. Am. J. Med. Genet. A 191, 1325\u20131338 (2023).","journal-title":"Am. J. Med. Genet. A"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-52691-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-52691-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-52691-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T17:09:07Z","timestamp":1706720947000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-52691-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,28]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["52691"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-52691-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,28]]},"assertion":[{"value":"20 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2330"}}