Next generation phenotyping in Emanuel and Pallister‐Killian syndrome using computer‐aided facial dysmorphology analysis of 2D photos

Abstract

High throughput approaches are continuously progressing and have become a major part of clinical diagnostics. Still, the critical process of detailed phenotyping and gathering clinical information has not changed much in the last decades. Forms of next generation phenotyping (NGP) are needed to increase further the value of any kind of genetic approaches, including timely consideration of (molecular) cytogenetics during the diagnostic quest. As NGP we used in this study the facial dysmorphology novel analysis (FDNA) technology to automatically identify facial phenotypes associated with Emanuel (ES) and Pallister-Killian Syndrome (PKS) from 2D facial photos. The comparison between ES or PKS and normal individuals expressed a full separation between the cohorts. Our results show that NPG is able to help in the clinic, and could reduce the time patients spend in diagnostic odyssey. It also helps to differentiate ES or PKS from each other and other patients with small supernumerary marker chromosomes, especially in countries with no access to more sophisticated genetic approaches apart from banding cytogenetics. Inclusion of more facial pictures of patient with sSMC, like isochromosome-18p-, cat-eye-syndrome or others may contribute to higher detection rates in future.

From gestalt to gene: early predictive dysmorphic features of PMM2-CDG

“…In the era of next-generation sequencing and next-generation phenotyping, there remains a lack of detailed PMM2-CDG phenotypic description based on a quantitative approach comparing to normal measures. Extensive descriptions through automated facial analysis software such as Face2Gene (https://www.face2gene.com/) may be very helpful to clinicians 15. It should be noted that pattern recognition through facial images in inborn errors of metabolism is a growing field 16–18…”

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

“…They conclude that DeepGestalt can be used to suggest a diagnostic direction in clinical practice. Several studies have employed DeepGestalt to diagnose rare syndromes with a moderate [46] to high degree of success, namely, Cornelia de Lange syndrome (ORPHA: 199) [47], Emanuel syndrome (ORPHA:96170), and Pallister-Killian syndrome (ORPHA: 884) [48]. Besides diagnosis, DeepGestalt can also be applied to discover new RDs [49].…”

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces

“…For example, facial structural deformities induced by congenital diseases can be representative of specific conditions [114] and eventually used as classification criteria. In this context, computer-assisted tools have tried to classify people that present certain conditions, such as Down's syndrome [115], the Cornelia de Lange syndrome [116], or other syndromes [117] in an automatic way. Despite Down's syndrome having a defined set of facial features, individuals affected by the syndrome will generally present just seven or eight of them [118].…”

“…facial morphology abnormalities / characteristics Down syndrome 1 [11], [115], [119]- [122] disturbances in facial expressions depression 2 [161], [162] schizophrenia 4 [15] ADHD 2, 5 [141] acromegaly 1 [180], [181] autism 2,5 [141] craniofacial deformations 1,4 [13], [136] schizophrenia 2,3 [163], [164] fetal alcohol exposure 1, 4 [12], [123]- [131], [135], [136] abnormal heat in orbital area Graves' ophthalmopathy 3 [26], [185] Cornelia de Lange syndrome 1 [116], [133], [134] nonspecific condition (Ophtalmology) 3 [186] Cushing's syndrome 1 [181], [182] other syndromes 1, 4 [117], [133], [134], [136], [137] asymmetry + synkinesis facial nerve dysfunction 1, 2 [86], [88], [89], [104], [105] facial asymmetry facial paralysis 1,5 [83]- [85], [97]- [100],…”