Gonadotropin administration to mimic mini-puberty in hypogonadotropic males: pump or injections?

Objective: Newborns with congenital hypogonadotropic hypogonadism (CHH) have an impaired postnatal activation of the gonadotropic axis. Substitutive therapy with recombinant gonadotropins can be proposed to mimic physiological male mini-puberty during the first months of life. The aim of this study was to co mpare the clinical and biological efficacy of two treatment modalities of gonadotropins administration during mini-puberty in CHH neonates. Design: Multicenter retrospective analytical epidemiological study comparing two treatments, pump vs injection, between 2004 and 2019. Methods: Clinical (penile size, testis size, testicular descent) and biological parameters (serum concentrations of testosterone, anti-Müllerian hormone (AMH) and Inhibin B) were compared between the two groups by multivariate analyses. Results: Thirty-five patients were included. A significantly higher incre ase in penile length and testosterone level was observed in the injection group compared to the pump group (+0.16 ± 0.02 mm vs +0.10 ± 0.02 mm per day, P = 0.002; and +0.04 ± 0.007 ng/mL vs +0.01 ± 0.008 ng/mL per day, P = 0.001). In both groups, significant increases in penile length and width, testosterone, AMH, and Inhibin B levels were observed, as well as improved testicular descent (odds ratio of not being in a scrotal position at the end of treatment = 0.97 (0.96; 0.99)). Conclusions: Early postnatal administration of recombinant gonadotropins in CHH boys is effective in stimulating penile growth, Sertoli cell proliferati on, and testicular descent, with both treatment modalities

Maxillary shape after primary cleft closure and before alveolar bone graft in two different management protocols: A comparative morphometric study

AIM AND SCOPE: Result assessment in cleft surgery is a technical challenge and requires the development of dedicated morphometric tools. Two cohorts of patients managed according to two different protocols were assessed at similar ages and their palatal shape was compared using geometric morphometrics. MATERIAL AND METHODS: Ten patients (protocol No. 1) benefited from early lip closure (1-3 months) and secondary combined soft and hard palate closure (6-9 months); 11 patients (protocol No. 2) benefited from later combined lip and soft palate closure (6 months) followed by hard palate closure (18 months). Cone-Beam Computed Tomography (CBCT) images were acquired at 5 years of age and palatal shapes were compared between protocols No. 1 and No. 2 using geometric morphometrics. RESULTS: Protocols No. 1 and No. 2 had a significantly different timing in their surgical steps but were assessed at a similar age (5 years). The inter-canine distance was significantly narrower in protocol No. 1. Geometric morphometrics showed that the premaxillary region was located more inferiorly in protocol No. 1. CONCLUSION: Functional approaches to cleft surgery (protocol No. 2) allow obtaining larger inter-canine distances and more anatomical premaxillary positions at 5 years of age when compared to protocols involving early lip closure (protocol No. 1). This is the first study comparing the intermediate results of two cleft management protocols using 3D CBCT data and geometric morphometrics. Similar assessments at the end of puberty are required in order to compare the long-term benefits of functional protocols

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes

IntroductionMandibulo-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.MethodsThe training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.ResultsWe trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] (p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648–0.920] (p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544–0.960] (p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses).ConclusionThis is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers

AI-based diagnosis and phenotype – Genotype correlations in syndromic craniosynostoses

Apert (AS), Crouzon (CS), Muenke (MS), Pfeiffer (PS), and Saethre Chotzen (SCS) are among the most frequently diagnosed syndromic craniosynostoses. The aims of this study were (1) to train an innovative model using artificial intelligence (AI)–based methods on two-dimensional facial frontal, lateral, and external ear photographs to assist diagnosis for syndromic craniosynostoses vs controls, and (2) to screen for genotype/phenotype correlations in AS, CS, and PS. We included retrospectively and prospectively, from 1979 to 2023, all frontal and lateral pictures of patients genetically diagnosed with AS, CS, MS, PS and SCS syndromes. After a deep learning–based preprocessing, we extracted geometric and textural features and used XGboost (eXtreme Gradient Boosting) to classify patients. The model was tested on an independent international validation set of genetically confirmed patients and non-syndromic controls. Between 1979 and 2023, we included 2228 frontal and lateral facial photographs corresponding to 541 patients. In all, 70.2% [0.593–0.797] (p &lt; 0.001) of patients in the validation set were correctly diagnosed. Genotypes linked to a splice donor site of FGFR2 in Crouzon-Pfeiffer syndrome (CPS) caused a milder phenotype in CPS. Here we report a new method for the automatic detection of syndromic craniosynostoses using AI.</p

AIDY : application de l'Intelligence Artificielle à la Dysmorphologie

Background. Thirty to 40% of the 7000 rare diseases present with craniofacial anomalies. Identifying these facial features requires the expert eye of dysmorphologist, and diagnosis in this field is based on experience. Hence, there have been a recent increase in the number of publications dedicated to the automatic diagnosis of rare conditions using facial photographs, an approach termed Next Generation Phenotyping (NGP). We aimed to evaluate the performances of a new NGP method on 2D photographs on an unprecedentedly large database, with a wide array of genetically proven syndromes, of various ages, genders and ethnicities. Methods. We included pictures from the photographic database of the maxillofacial surgery and plastic surgery department and from the medical genetics department of Hôpital Necker - Enfants Malades (AP-HP), Paris, France. This database contains 594,000 photographs from 22,000 patients followed in the department since 1981. The writing of this work is based on the different stages in the construction of this new tool. We first described the first stage in analyzing photographs: (1) automatically detecting regions of interest, i.e. front and profile faces and external ears; and (2) automatically placing a series of landmarks on these regions. We then used a combination of shape analysis methods, or geometric morphometrics, and texture analysis on key areas of the face. Finally, these geometric and textural parameters were used to train machine learning models based on a XGboost classifier. These models were validated on independent data, from other national (Nantes, Lille, Montpellier) and international (London, Bangkok) hospitals. Three main types of deliverables were obtained: (1) diagnostic performances for one or more genetic syndromes, (2) phenotype-genotype correlations for certain syndromes with multiple genetic variants, and (3) an analysis of the effects of surgery or drugs on facial morphology. Results. Object recognition was optimized with the Faster R-Convolutional Neural Network (CNN) based detector. The best landmarking model was the patch-based Active Appearance Model (AAM) and was able to significantly distinguish patients with Treacher Collins (TC) syndrome from control non-syndromic patients (p < 0.001). We were then able to train a detection model for Guion Almeida syndrome (Mandibulofacial Dysostosis with Microcephaly, MFDM), based on geometric morphometrics of the external ear, with an accuracy of 0.969 [0.838 - 0.999] (p < 0.001) among non-syndromic controls, and 0.813 [0.544 - 0.960] (p = 0.003) among 3 differential diagnoses of this condition. Then, the incorporation of frontal and profile facial analysis, as well as texture analysis, enabled the diagnosis of Apert, Crouzon and Pfeiffer syndromes with respective accuracies of 0.879 [0.718 - 0.966] (p < 0.001), 0.932 [0.813 - 0.986] (p < 0.001) and 1.000 [0.815 - 1.000] (p < 0.001). Conclusion. We were able to build a robust NGP tool, enabling automated analysis of the facial phenotype on 2D photographs of children with various genetic syndromes. We will now extend this algorithm, whose methodology has been completed and validated, to the analysis of the total number of syndromes in our database.Contexte. Trente à quarante pourcents des 7000 maladies rares entraînent des anomalies crânio-faciales et certaines nécessitent l'œil expert d'un dysmorphologiste. Le diagnostic, dans cette discipline, est basé avant tout sur l'expérience du praticien. Pour cette raison, les publications scientifiques décrivant des méthodes de diagnostic automatique de ces maladies rares à partir de photographies 2D de visages se multiplient. Ces méthodes peuvent être regroupées sous le terme de Phénotypage Nouvelle Génération (PNG). Ce travail de thèse a pour objectif de décrire de décrire les performances d'un nouvel outil de PNG basé sur des photographies 2D de visages d'enfants, entraîné sur une base non publiée, de grande échelle, et d'une grande diversité d'âges, de genres et d'ethnies. Méthodes. Nous avons inclus les données photographiques des services de chirurgie maxillo-faciale et de génétique clinique de l'hôpital Necker Enfants Malades (Paris, France). Cette base comprend 594 000 photographies correspondant à 22 000 patients, suivis depuis 1981. L'écriture de ce travail repose sur la publication successive des différentes étapes de construction de ce nouvel outil. Tout d'abord, nous décrivons la première étape d'analyse des photographies, consistant 1) à détecter automatiquement nos régions d'intérêt, c'est-à-dire les visages de face, de profil et les oreilles externes ; et 2) à placer automatiquement une série de landmarks, ou points anatomiques repères sur ces régions. Puis, nous utilisons la combinaison de méthodes d'analyse de formes ou morphométrie géométrique, et une analyse de textures sur des zones clés du visage. Enfin, ces paramètres géométriques et texturaux permettent d'entraîner des modèles de machine learning basés sur le classificateur XGboost. Ces modèles ont alors été validés sur des données indépendantes du set d'entraînement, d'autres centres hospitaliers nationaux (Nantes, Lille, Montpellier) et internationaux (Londres, Bangkok). Trois grands types de résultats ont pu être obtenus à travers les différentes publications au cours de cette thèse de doctorat : 1) des performances diagnostiques concernant un ou plusieurs syndromes génétiques, 2) des corrélations phénotype - génotypes pour certains syndromes pour lesquels il existe plusieurs variations génétiques et 3) une analyse des effets d'un traitement chirurgical ou médicamenteux sur la morphologie faciale. Résultats. Notre méthode a permis l'obtention de différents résultats de performances croissantes après incorporation des différentes étapes. La détection de régions d'intérêt a été optimisée par un algorithme de type Faster R-Convolutional Neural Network (CNN). Le meilleur modèle de landmarking était le patch-based Active Appearance Model (AAM), permettant une détection de patients atteints du syndrome de Treacher Collins parmi des enfants contrôles non-syndromiques (p < 0.001). Nous avons ensuite pu entrainer un modèle de détection du syndrome de Guion Almeida (Mandibulofacial Dysostosis with Microcephaly, MFDM), basé sur une analyse par morphométrie géométrique de l'oreille externe, avec une précision de 0.969 [0.838 - 0.999] (p < 0.001) parmi des contrôles, et de 0.813 [0.544 - 0.960] (p = 0.003) parmi 3 diagnostics différentiels de cette affection. Ensuite, l'incorporation de l'analyse du visage de face et de profil, ainsi que l'analyse des textures, a permis le diagnostic des syndromes d'Apert, de Crouzon et de Pfeiffer avec des précisions respectives de 0.879 [0.718 - 0.966] (p < 0.001), 0.932 [0.813 - 0.986] (p < 0.001) et 1.000 [0.815 - 1.000] (p < 0.001). Conclusion. Nous avons pu construire un outil robuste de NGP, permettant une analyse automatisée du phénotype facial sur des photographies 2D d'enfants atteints de syndromes génétiques. Nous devons désormais étendre cet algorithme à la méthodologie aboutie et validée, à l'analyse du grand nombre de syndromes présents dans notre base de données

Oral migration of

We report an autochthonous case of oral dirofilariasis in a 46-year-old female patient exposed in South-Eastern France. The patient first presented eyelid creeping dermatitis of one-week duration, then a sub-mucosal nodule appeared in the cheek. The entire nodule was removed surgically. Histologically, the nodule appeared as inflammatory tissue in which a worm was seen. The molecular analysis, based on cox1 and 12S sequences, identified Dirofilaria repens. Ivermectin treatment was given prior to diagnosis, while taking into consideration the most common causes of creeping dermatitis, but treatment was ineffective. The oral form of dirofilariasis is uncommon and could lead to diagnostic wandering

Evaluation of Electric Scooter Head and Neck Injuries in Paris, 2017-2019

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Intentional craniofacial remodelling in Europe in the XIXth century: Quantitative evidence of soft tissue modifications from Toulouse, France

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