Identifying facial phenotypes of genetic disorders using deep learning

By Yaron Gurovich, Yair Hanani, Omri Bar, Guy Nadav, N. Fleischer, D. Gelbman, Lina Basel-Salmon, P. Krawitz, S. Kamphausen, M. Zenker and 2 more. Nature Medicine, 2019

624 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/67ce92d6a45e99cb609fe4fcbc38383213afc0f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Wei-Qi Wei
2. Raquel Dias
3. Ali Torkamani

In Molecular Biology

1. Wei-Qi Wei
2. Raquel Dias
3. Mustafa Suleyman
4. Ali Torkamani

Citations in the corpus

• Precision Medicine, AI, and the Future of Personalized Health Care (2020, methodology): “78 For example, accurate and fast diagnosis for seriously ill infants that have a suspected genetic disease can be attained by using rapid whole-genome sequencing and NLP-enabled automated phenotyping.”
• Key challenges for delivering clinical impact with artificial intelligence (2019, background): “Furthermore, AI has aided in polyp detection from colonoscopy [28], improving genomics interpretation [29], identifying genetic conditions from facial appearance [30], and assessing embryo quality to maximise the success of in vitro fertilisation [31].”
• Artificial intelligence in clinical and genomic diagnostics (2019, background): “Similarly, facial image recognition can be used to identify rare genetic disorders and to guide molecular diagnoses [19, 20].”
• Deep Face Recognition: A Survey (2018, background): “Facial images can predict not only demographic information such as gender, age, or race, but even the genetic information [297].”