KaydedilenlerDerginizi ekleyin

Dergiler / The European Research Journal / 2022 / Cilt: 8 - Sayı: 2

The road from mutation to next generation phenotyping: contribution of deep learning technology (Face2Gene) to diagnosis neurofibromatosis type 1

Muhsin ELMAS
Başak GÖĞÜŞ
Sayfa
145–154
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Conclusion: Considering the developments in technology in the last 10 years, it is thought that artificial intelligence applications such as Face2Gene will be used a lot in the routines of medical doctors in the next 10 years.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Objectives: Genetics is one of the fastest growing medical fields in the last 10 years.
📊
Temel Bulgular & Veri:While new analysis methods such as Next Generation Sequencing have been developed, the use of artificial intelligence like Face2Gene in this field has also been developed.
💡
Sonuç & Katkı:Conclusion: Considering the developments in technology in the last 10 years, it is thought that artificial intelligence applications such as Face2Gene will be used a lot in the routines of medical doctors in the next 10 years.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Neurofibromatosis 1 · Cafe-au-Lait Spots · Deep Learning

Abstract

Objectives: Genetics is one of the fastest growing medical fields in the last 10 years. While new analysis methods such as Next Generation Sequencing have been developed, the use of artificial intelligence like Face2Gene in this field has also been developed. The aim of this study is to evaluate the clinical, genetic and dysmorphic findings of Neurofibromatosis type 1 (NF1) patients, a disease of the RASopathy group. At the same time, another aim of this study is to evaluate and compare with other RASopathies diseases the success of Face2Gene application which is one of the NGP technologies, in this group of diseases. Methods: This study is a retrospective archive scan. 14 patients from 3 different patient groups were selected for the study. Face2Gene analysis was performed for these groups. Detailed clinical, genetic and dysmorphic findings of NF1 patients were also examined. Results: As a result of the genetic analysis of NF1 patients, one patient had novel mutation. The most detected mutation type is nonsense mutation (42,8%). The most common finding in magnetic resonance imaging was hamartoma (29%). Face2Gene suggested that NF1 in top-3 for 10 of 14 NF1 patients. Additionally, at the comparison of NF1 patients and non-NF1 RASopathies patients resulted as AUC was 0.749 and p value was 0.134. Conclusion: Considering the developments in technology in the last 10 years, it is thought that artificial intelligence applications such as Face2Gene will be used a lot in the routines of medical doctors in the next 10 years.

🔖

Bu Makaleye Atıf Yap

ELMAS, M., GÖĞÜŞ, B. (2022). The road from mutation to next generation phenotyping: contribution of deep learning technology (Face2Gene) to diagnosis neurofibromatosis type 1. The European Research Journal, Cilt: 8 - Sayı: 2, 145–154. https://ajindex.com/articles/403208

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (15)

  1. Basel D
  2. Dysmorphology. In: Kliegman RM, Lye PS, Bordini BJ, Toth H, Basel D, editors. Nelson Pediatr. Symptom-Based Diagnosis. Elsevier; 2018. p. 393-410.e1
  3. Starbuck J. On the antiquity of trisomy 21: moving towards a quantitative diagnosis of Down syndrome in historic material culture. J Contemp Anthropol 2011;2:18-44
  4. Ruggieri M, Praticò AD, Caltabiano R, Polizzi A. Early history of the different forms of neurofibromatosis from ancient Egypt to the British Empire and beyond: first descriptions, medical curiosities, misconceptions, landmarks, and the persons behind the syndromes. Am J Med Genet A 2018;176:515-50
  5. Hart TC, Hart PS. Genetic studies of craniofacial anomalies: clinical implications and applications. Orthod Craniofac Res 2009;12:212-20
  6. Fischer C, Schweigert S, Spreckelsen C, Vogel F. Programs, databases, and expert systems for human geneticists--a survey. Hum Genet 1996;97:129-37
  7. Gurovich Y, Hanani Y, Bar O, Fleischer N, Gelbman D, Basel-salmon L, et al. DeepGestalt - identifying rare genetic syndromes using deep learning. arXiv 2017:1801-07637v1 7. Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N, Gelbman D, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med 2019;25:60-4
  8. Mishima H, Suzuki H, Doi M, Miyazaki M, Watanabe S. Evaluation of Face2Gene using facial images of patients with congenital dysmorphic syndromes recruited in Japan. J Hum Genet 2019;64:789-94
  9. Tidyman WE, Rauen KA. Pathogenetics of the RASopathies. Hum Mol Genet 2016;25:R123-32
  10. Evans DG, Howard E, Giblin C, Clancy T, Spencer H, Huson SM, et al. Birth incidence and prevalence of tumor-prone syndromes: estimates from a UK family genetic register service. Am J Med Genet 2010;152A:327-32
  11. Valero MC, Pascual-Castroviejo I, Velasco E, Moreno F, Hernández-Chico C. Identification of de novo deletions at the NF1 gene: no preferential paternal origin and phenotypic analysis of patients. Hum Genet 1997;99:720-6
  12. Boyd KP, Korf BR, Theos A. Neurofibromatosis type 1. J Am Acad Dermatol 2009;61:1-14
  13. Williams VC, Lucas J, Babcock MA, Gutmann DH, Bruce B, Maria BL. Neurofibromatosis type 1 revisited. Pediatrics 2009;123:124-33
  14. Valero MC, Martín Y, Hernández-Imaz E, Hernández AM, Meleán G, Valero AM, et al. A highly sensitive genetic protocol to detect NF1 mutations. J Mol Diagnostics 2011;13:113-22
  15. Pantel JT, Zhao M, Mensah MA, Hajjir N, Hsieh T-C, Hanani Y, et al. Advances in computer-assisted