Abstract

Among genodermatoses, trichothiodystrophies (TTDs) are a rare genetically heterogeneous group of syndromic conditions, presenting with skin, hair, and nail abnormalities. An extra-cutaneous involvement (craniofacial district and neurodevelopment) can be also a part of the clinical picture. The presence of photosensitivity describes three forms of TTDs: MIM#601675 (TTD1), MIM#616390 (TTD2) and MIM#616395 (TTD3), that are caused by variants afflicting some components of the DNA Nucleotide Excision Repair (NER) complex and with more marked clinical consequences. In the present research, 24 frontal images of paediatric patients with photosensitive TTDs suitable for facial analysis through the next-generation phenotyping (NGP) technology were obtained from the medical literature. The pictures were compared to age and sex-matched to unaffected controls using 2 distinct deep-learning algorithms: DeepGestalt and GestaltMatcher (Face2Gene, FDNA Inc., USA). To give further support to the observed results, a careful clinical revision was undertaken for each facial feature in paediatric patients with TTD1 or TTD2 or TTD3. Interestingly, a distinctive facial phenotype emerged by the NGP analysis delineating a specific craniofacial dysmorphic spectrum. In addition, we tabulated every single detail within the observed cohort. The novelty of the present research includes the facial characterization in children with the photosensitive types of TTDs through the 2 different algorithms. This result can become additional criteria for early diagnosis, and for subsequent targeted molecular investigations as well as a possible tailored multidisciplinary personalized management.

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