Identifying facial phenotypes of genetic disorders using deep learning.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
18
12
2017
accepted:
29
10
2018
entrez:
9
1
2019
pubmed:
9
1
2019
medline:
11
5
2019
Statut:
ppublish
Résumé
Syndromic genetic conditions, in aggregate, affect 8% of the population
Identifiants
pubmed: 30617323
doi: 10.1038/s41591-018-0279-0
pii: 10.1038/s41591-018-0279-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
60-64Références
Baird, P. A., Anderson, T., Newcombe, H. & Lowry, R. Genetic disorders in children and young adults: a population study. Am. J. Hum. Genet. 42, 677–693 (1988).
pubmed: 3358420
pmcid: 1715177
Hart, T. & Hart, P. Genetic studies of craniofacial anomalies: clinical implications and applications. Orthod. Craniofac. Res. 12, 212–220 (2009).
doi: 10.1111/j.1601-6343.2009.01455.x
Ferry, Q. et al. Diagnostically relevant facial gestalt information from ordinary photos. eLife 3, e02020 (2014).
doi: 10.7554/eLife.02020
Basel-Vanagaite, L. et al. Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis. Clin. Genet. 89, 557–563 (2016).
doi: 10.1111/cge.12716
Rai, M. C. E., Werghi, N., Al Muhairi, H. & Alsafar, H. Using facial images for the diagnosis of genetic syndromes: a survey. In 2015 International Conference on Communications, Signal Processing, and their Applications (ICCSPA) (2015).
Shukla, P., Gupta, T., Saini, A., Singh, P. & Balasubramanian, R. A deep learning frame-work for recognizing developmental disorders. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE, 2017).
Hadj-Rabia, S. et al. Automatic recognition of the XLHED phenotype from facial images. Am. J. Med. Genet. A. 173, 2408–2414 (2017).
doi: 10.1002/ajmg.a.38343
Valentine, M. et al. Computer-aided recognition of facial attributes for Fetal Alcohol Spectrum disorders. Pediatrics 140, e20162028 (2017).
doi: 10.1542/peds.2016-2028
Gripp, K. W., Baker, L., Telegrafi, A. & Monaghan, K. G. The role of objective facial analysis using FDNA in making diagnoses following whole exome analysis. Report of two patients with mutations in the BAF complex genes. Am. J. Med. Genet. A. 170, 1754–1762 (2016).
doi: 10.1002/ajmg.a.37672
Delgadillo, V., Maria del Mar, O., Gort, L., Coll, M. J. & Pineda, M. Natural history of Sanfilippo syndrome in Spain. Orphanet J. Rare Dis. 8, 189 (2013).
doi: 10.1186/1750-1172-8-189
Kole, A. et al. The Voice of 12,000 Patients: experiences and expectations of rare disease patients on diagnosis and care in Europe. Eurordis http://www.eurordis.org/IMG/pdf/voice_12000_patients/EURORDISCARE_FULLBOOKr.pdf (2009).
Taigman, Y., Yang, M., Ranzato, M. & Wolf, L. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2014 1701–1708 (IEEE, 2014).
Huang, G. B., Ramesh, M., Berg, T. & Learned-Miller, E. Labeled faces in the Wild: a database for studying face recognition in unconstrained environments. In Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition (2008).
Yi, D., Lei, Z., Liao, S. & Li, S. Z. Learning face representation from scratch. Preprint at https://arxiv.org/abs/1411.7923 (2014).
Schroff, F., Kalenichenko, D. & Philbin, J. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2015 815–823 (IEEE,2015).
Li, H., Lin, Z., Shen, X., Brandt, J. & Hua, G. A convolutional neural network cascade for face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2015 5325–5334 (IEEE, 2015).
Karlinsky, L. & Ullman, S. Using linking features in learning non-parametric part models. Computer Vision–ECCV 2012, 326–339 (2012).
Rohatgi, S. et al. Facial diagnosis of mild and variant CdLS: Insights from a dysmorphologist survey. Am. J. Med. Genet. A. 152, 1641–1653 (2010).
doi: 10.1002/ajmg.a.33441
Bird, L. M., Tan, W. H. & Wolf, L. The role of computer-aided facial recognition technology in accelerating the identification of Angelman syndrome. In 35th Annual David W Smith Workshop (2014).
Allanson, J. E. et al. The face of Noonan syndrome: does phenotype predict genotype. Am. J. Med. Genet. A. 152, 1960–1966 (2010).
doi: 10.1002/ajmg.a.33518
Gulec, E. Y., Ocak, Z., Candan, S., Ataman, E. & Yarar, C. Novel mutations in PTPN11 gene in two girls with Noonan syndrome phenotype. Int. J. Cardiol. 186, 13–15 (2015).
doi: 10.1016/j.ijcard.2015.03.260
Zenker, M. et al. SOS1 is the second most common Noonan gene but plays no major role in cardio-facio-cutaneous syndrome. J. Med. Genet. 44, 651–656 (2007).
doi: 10.1136/jmg.2007.051276
Rusu, C., Idriceanu, J., Bodescu, I., Anton, M. & Vulpoi, C. Genotype-phenotype correlations in Noonan Syndrome. Acta Endocrinologica 10, 463–476 (2014).
doi: 10.4183/aeb.2014.463
Cavé, H. et al. Mutations in RIT1 cause Noonan syndrome with possible juvenile myelomonocytic leukemia but are not involved in acute lymphoblastic leukemia. Eur. J. Hum. Genet. 24, 1124–1131 (2016).
doi: 10.1038/ejhg.2015.273
Kouz, K. et al. Genotype and phenotype in patients with Noonan syndrome and a RIT1 mutation. Genet. Med. 18, 1226–1234 (2016).
doi: 10.1038/gim.2016.32
Winter, R. M. & Baraitser The London Dysmorphology Database. J. Med. Genet. 24, 509–510 (1987).
doi: 10.1136/jmg.24.8.509
Robinson, P. N. & Mundlos, S. The human phenotype ontology. Clin. Genet. 77, 525–534 (2010).
doi: 10.1111/j.1399-0004.2010.01436.x
Köhler, S. et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. A. J. Hum. Genet. 85, 457–464 (2009).
doi: 10.1016/j.ajhg.2009.09.003
Zarate, Y. A. et al. Natural history and genotype-phenotype correlations in 72 individuals with SATB2-associated syndrome. Am. J. Med. Genet. A. 176, 925–935 (2018).
doi: 10.1002/ajmg.a.38630
Liehr, T. et al. Next generation phenotyping in Emanuel and Pallister Killian Syndrome using computer-aided facial dysmorphology analysis of 2D photos. Clin. Genet. 93, 378–381 (2017).
doi: 10.1111/cge.13087
Hennekam, R. & Biesecker, L. G. Next-generation sequencing demands next-generation phenotyping. Hum. Mutat. 33, 884–886 (2012).
doi: 10.1002/humu.22048
Huang, G., Mattar, M., Lee, H. & Learned-Miller, E. G. Learning to align from scratch. In Advances in Neural Information Processing Systems 2012 764–772 (2012).
Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 2014 3320–3328 (2014).
Taigman, Y., Yang, M., Ranzato, M. & Wolf, L. Web-scale training for face identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2015 2746–2754 (IEEE, 2015).
Zhou, E., Cao, Z. & Yin, Q. Naive-deep face recognition: touching the limit of LFW benchmark or not? Preprint at https://arxiv.org/abs/1501.04690 (2015).
Liu, J., Deng, Y., Bai, T., Wei, Z. & Huang, C. Targeting ultimate accuracy: face recognition via deep embedding. Preprint at https://arxiv.org/abs/1506.07310 (2015).
Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. Preprint at https://arxiv.org/abs/1312.6034 (2013).
Parkhi, O. M., Vedaldi, A. & Zisserman, A. Deep face recognition. In Proceedings of the British Machine Vision Conference 1, 6 (2015).
Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proc. International Conference on Machine Learning 2015 448–456 (2015).
Chollet, F. et al. Keras. http://keras.io (2015).
Abadi, M. et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems. Preprint at https://arxiv.org/abs/1603.04467 (2016).
He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In Proc. IEEE International Conference on Computer Vision 1026–1034 (IEEE, 2015).
Kingma, D. and Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
Glorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international Conference on Artificial Intelligence and Statistics 249–256 (2010).
Efron, B. Bootstrap methods: another look at the jackknife. In Breakthroughs in Statistics 569–593 (Springer, New York, 1992).