Extending Phenotypic Spectrum of 17q22 Microdeletion: Growth Hormone Deficiency

Durmaz, Ceren Damla; Altıner, Şule; Taşdelen, Elifcan; Karabulut, Halil Gürhan; Ruhi, Hatice Ilgın

doi:10.1080/15513815.2019.1710789

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…With the development of craniofacial genetics, many rare genetic diseases have also been shown to have specific facial features, such as the broad nasal bridge in 15q11-q13 duplication syndrome, the maxillary hypoplasia in 17q22 microdeletion, the wide and depressed nasal bridge in Helsmoortel-van der AA syndrome, etc. [13][14][15]. These studies show the specificity of fetal facial features.…”

Section: Introductionmentioning

confidence: 53%

“…All examinations have been performed and diagnosed before the research by one or two ultrasound specialists with more than five years of experience. Overall, our datasets contain 845 normal pregnancy images and 275 genetic disease images from 667 cases, with a gestational age distribution of 14.8 ± 2.6 (range [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Details on the development set and test set are described in Table 1.…”

Section: Data Acquisitionmentioning

confidence: 99%

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The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases

Tang

Han

Xie

et al. 2023

IJERPH

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With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to healthcare. The existing research primarily focuses on using teenagers’ or adults’ face information to screen for genetic diseases, but there are no relevant directions on disease detection using fetal facial information. To fill the vacancy, we designed a two-stage ensemble learning model based on sonography, Fgds-EL, to identify genetic diseases with 932 images. Concretely speaking, we use aggregated information of facial regions to detect anomalies, such as the jaw, frontal bone, and nasal bone areas. Our experiments show that our model yields a sensitivity of 0.92 and a specificity of 0.97 in the test set, on par with the senior sonographer, and outperforming other popular deep learning algorithms. Moreover, our model has the potential to be an effective noninvasive screening tool for the early screening of genetic diseases in the fetus.

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Section: Introductionmentioning

confidence: 53%

Section: Data Acquisitionmentioning

confidence: 99%

The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases

Tang

Han

Xie

et al. 2023

IJERPH

View full text Add to dashboard Cite

show abstract

“…Individuals with 1.8–2.5 Mb microdeletions of the 17q22 region have been reported in the literature. 60 , 61 , 62 , 63 , 64 An important causal gene related to the clinical manifestations of 17q22 microdeletion is NOG (MIM: 602991 ). When it is included in the microdeletion, NOG -related bone and joint features such as symphalangism, conductive hearing loss, and joint contractures are present, as are visual impairment and facial dysmorphic features.…”

Section: Discussionmentioning

confidence: 99%

“… 60 Among the reported individuals with 17q22 contiguous microdeletions, six had loss of SRSF1 and presented with syndromic ID. 60 , 61 , 62 , 63 More recently, Pang et al. reported a family with 1.6 Mb microdeletion in chromosome 17q22 with NOG -related symphalangism spectrum disorder including conductive hearing loss, proximal symphalangism of the fifth fingers, small palpebral fissures, broadened hemicylindrical nose with a bulbous tip, amblyopia, and strabismus without ID or any other neurodevelopmental abnormalities.…”

Section: Discussionmentioning

confidence: 99%