Publications by authors named "E Galliani"
Superior orbital frontal clefts are one of the rare craniofacial clefts described by Tessier in 1976, and occur most often sporadically. They are numbered 9, 10 and 11 in this classification, and are located respectively laterally, in the middle and medially to the upper part of the orbit. Their clinical expression is variable on soft tissue and bone, with possible dissociation of involvement.
View Article and Find Full Text PDFPurpose: Patients with syndromic hemifacial microsomia (SHFM) are at risk of obstructive sleep apnea (OSA). The aim of the study was to describe the prevalence of OSA and its management, especially in patients with Goldenhar syndrome (GS).
Methods: The respiratory polygraphies and clinical management of 15 patients, aged 2 to 23 years, evaluated at a national reference center, were analyzed.
Article Synopsis
- The study aims to create a new AI-based model for identifying Kabuki Syndrome (KS) from 2D facial photos, differentiating between its two types: KS1 (KMT2D-related) and KS2 (KDM6A-related).
- Utilizing over 1,400 facial images from 634 patients and controls, researchers incorporated machine learning techniques, specifically XGboost, for improved predictive accuracy.
- The proposed model achieved an impressive accuracy of 95.8% in identifying KS and showed better performance than existing AI solutions and expert evaluations.
Background: Syngnathia is an ultrarare craniofacial malformation characterised by an inability to open the mouth due to congenital fusion of the upper and lower jaws. The genetic causes of isolated bony syngnathia are unknown.
Methods: We used whole exome and Sanger sequencing and microsatellite analysis in six patients (from four families) presenting with syngnathia.
Introduction: Mandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.
Methods: The training set contained 1,592 ear photographs, corresponding to 550 patients.