Automated syndrome diagnosis by three-dimensional facial imaging.
Genet Med
; 22(10): 1682-1693, 2020 10.
Article
en En
| MEDLINE
| ID: mdl-32475986
ABSTRACT
PURPOSE:
Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.METHODS:
We analyzed variation in three-dimensional (3D) facial images of 7057subjects:
3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.RESULTS:
Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.CONCLUSION:
Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Imagenología Tridimensional
/
Cara
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Año:
2020
Tipo del documento:
Article