Clinical Decision Making in Dysmorphology- Emerging Role of Artificial Intelligence
DOI:
https://doi.org/10.14738/jbemi.95.13309Keywords:
artificial intelligence, dysmorphism, facial recognition technology, genetic disordersAbstract
The human genome codes for more than 22,000 genes, many of which have been implicated in human diseases. These genetic diseases are often associated with dysmorphic facial features. Dysmorphic features occur due to premature closure of cranial sutures resulting in changes in skull shape and facial characteristics. Assessment of dysmorphic features is a crucial component of genetic consultations. This requires a great deal of clinical experience and expertise and tends to be subjective. Artificial intelligence-based analysis can come in handy for quick and accurate identification of dysmorphic features. This review explores the role played by artificial intelligence in identifying dysmorphic facies and diagnosing various genetic diseases in children.
References
. Gurovich Y, Hanani Y, Bar O, Fleischer N, Gelbman D, Basel-Salmon L, et al. DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning. 2018 Jan 23; Available from: http://arxiv.org/abs/1801.07637
. Bannister JJ, Crites SR, Aponte JD, Katz DC, Wilms M, Klein OD, et al. Fully automatic landmarking of syndromic 3D facial surface scans using 2D images. Sensors (Switzerland). 2020 Jun 1;20(11):1–14.
. Zhao Q, Okada K, Rosenbaum K, Kehoe L, Zand DJ, Sze R, et al. Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ICA. Med Image Anal. 2014;18(5):699–710.
. Čaplovičová M, Moslerová V, Dupej J, Macek M, Zemková D, Hoffmannová E, et al. Modeling age-specific facial development in Williams–Beuren-, Noonan-, and 22q11.2 deletion syndromes in cohorts of Czech patients aged 3–18 years: A cross-sectional three-dimensional geometric morphometry analysis of their facial gestalt. Am J Med Genet A. 2018 Dec 1;176(12):2604–13.
. Geremek M, Szklanny K. Deep learning-based analysis of face images as a screening tool for genetic syndromes. Sensors. 2021 Oct 1;21(19).
. Hurst, Anna C.E.. Facial recognition software in clinical dysmorphology. Current Opinion in Pediatrics: December 2018 - Volume 30 - Issue 6 - p 701-706. doi: 10.1097/MOP.0000000000000677
. Gomez DA, Bird LM, Fleischer N, Abdul-Rahman OA. Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning. Am J Med Genet A. 2020 Sep 1;182(9):2021–6.
. Su Z, Liang B, Shi F, Gelfond J, Šegalo S, Wang J, et al. Deep learning-based facial image analysis in medical research: A systematic review protocol. Vol. 11, BMJ Open. BMJ Publishing Group; 2021.
. Basel-Vanagaite L, Wolf L, Orin M, Larizza L, Gervasini C, Krantz ID, et al. Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis. Clin Genet. 2016 May 1;89(5):557–63.
. Özdemir ME, Telatar Z, Eroğul O, Tunca Y. Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree. Australas Phys Eng Sci Med. 2018 Jun 1;41(2):451–61.
. Li X, Yao R, Tan X, Li N, Ding Y, Li J, et al. Molecular and phenotypic spectrum of Noonan syndrome in Chinese patients. Clin Genet. 2019 Oct 1;96(4):290–9.
. Latorre-Pellicer A, Ascaso Á, Trujillano L, Gil-Salvador M, Arnedo M, Lucia-Campos C, et al. Evaluating face2gene as a tool to identify cornelia de lange syndrome by facial phenotypes. Int J Mol Sci. 2020 Feb 1;21(3).
. Hadj-Rabia S, Schneider H, Navarro E, Klein O, Kirby N, Huttner K, et al. Automatic recognition of the XLHED phenotype from facial images. Am J Med Genet A. 2017 Sep 1;173(9):2408–14.
. Martinez-Monseny A, Cuadras D, Bolasell M, Muchart J, Arjona C, Borregan M, et al. From gestalt to gene: Early predictive dysmorphic features of PMM2-CDG. J Med Genet. 2019 Apr 1;56(4):236–45.
. Chen S, Pan ZX, Zhu HJ, Wang Q, Yang JJ, Lei Y, et al. Development of a computer-aided tool for the pattern recognition of facial features in diagnosing Turner syndrome: Comparison of diagnostic accuracy with clinical workers. Sci Rep. 2018 Dec 1;8(1).
. Valentine M, Bihm DC, Wolf L, Eugene Hoyme H, May PA, Buckley D, et al. Computer-Aided Recognition of Facial Attributes for Fetal Alcohol Spectrum Disorders.
. Pascolini G, Fleischer N, Ferraris A, Majore S, Grammatico P. The facial dysmorphology analysis technology in intellectual disability syndromes related to defects in the histones modifiers. J Hum Genet. 2019 Aug 1;64(8):721–8.
. Myers L, Anderlid BM, Nordgren A, Lundin K, Kuja-Halkola R, Tammimies K, et al. Clinical versus automated assessments of morphological variants in twins with and without neurodevelopmental disorders. Am J Med Genet A. 2020 May 1;182(5):1177–89.
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Copyright (c) 2022 Nayesha Mahwish, Batul Abdeali Saherawala, Malay Jhancy
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