Williams-Beuren syndrome (WBS) is a rare genetic disorder characterized by special facial gestalt, delayed development, and supravalvular aortic stenosis or/and stenosis of the branches of the pulmonary artery. We aim to develop and optimize accurate models of facial recognition to assist in the diagnosis of WBS, and to evaluate their effectiveness by using both five-fold cross-validation and an external test set. We used a total of 954 images from 135 patients with WBS, 124 patients suffering from other genetic disorders, and 183 healthy children. The training set comprised 852 images of 104 WBS cases, 91 cases of other genetic disorders, and 145 healthy children from September 2017 to December 2021 at the Guangdong Provincial People\'s Hospital. We constructed six binary classification models of facial recognition for WBS by using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. Transfer learning was used to pre-train the models, and each model was modified with a variable cosine learning rate. Each model was first evaluated by using five-fold cross-validation and then assessed on the external test set. The latter contained 102 images of 31 children suffering from WBS, 33 children with other genetic disorders, and 38 healthy children. To compare the capabilities of these models of recognition with those of human experts in terms of identifying cases of WBS, we recruited two pediatricians, a pediatric cardiologist, and a pediatric geneticist to identify the WBS patients based solely on their facial images. We constructed six models of facial recognition for diagnosing WBS using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. The model based on VGG-19BN achieved the best performance in terms of five-fold cross-validation, with an accuracy of 93.74% ± 3.18%, precision of 94.93% ± 4.53%, specificity of 96.10% ± 4.30%, and F1 score of 91.65% ± 4.28%, while the VGG-16BN model achieved the highest recall value of 91.63% ± 5.96%. The VGG-19BN model also achieved the best performance on the external test set, with an accuracy of 95.10%, precision of 100%, recall of 83.87%, specificity of 93.42%, and F1 score of 91.23%. The best performance by human experts on the external test set yielded values of accuracy, precision, recall, specificity, and F1 scores of 77.45%, 60.53%, 77.42%, 83.10%, and 66.67%, respectively. The F1 score of each human expert was lower than those of the EfficientNet-b3 (84.21%), ResNet-50 (74.51%), VGG-16 (85.71%), VGG-16BN (85.71%), VGG-19 (83.02%), and VGG-19BN (91.23%) models.
CONCLUSIONS: The results showed that facial recognition technology can be used to accurately diagnose patients with WBS. Facial recognition models based on VGG-19BN can play a crucial role in its clinical diagnosis. Their performance can be improved by expanding the size of the training dataset, optimizing the CNN architectures applied, and modifying them with a variable cosine learning rate.
BACKGROUND: • The facial gestalt of WBS, often described as \"elfin,\" includes a broad forehead, periorbital puffiness, a flat nasal bridge, full cheeks, and a small chin. • Recent studies have demonstrated the potential of deep convolutional neural networks for facial recognition as a diagnostic tool for WBS.
BACKGROUND: • This study develops six models of facial recognition, EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN, to improve WBS diagnosis. • The VGG-19BN model achieved the best performance, with an accuracy of 95.10% and specificity of 93.42%. The facial recognition model based on VGG-19BN can play a crucial role in the clinical diagnosis of WBS.

The advancement of genetic knowledge and the discovery of an increasing number of genetic disorders has made the role of the geneticist progressively more complex and fundamental. However, most genetic disorders present during childhood; thus, their early recognition is a challenge for the pediatrician, who will be also involved in the follow-up of these children, often establishing a close relationship with them and their families and becoming a referral figure. In this review, we aim to provide the pediatrician with a general knowledge of the approach to treating a child with a genetic syndrome associated with dysmorphic features. We will discuss the red flags, the most common manifestations, the analytic collection of the family and personal medical history, and the signs that should alert the pediatrician during the physical examination. We will offer an overview of the physical malformations most commonly associated with genetic defects and the way to describe dysmorphic facial features. We will provide hints about some tools that can support the pediatrician in clinical practice and that also represent a useful educational resource, either online or through apps downloaded on a smartphone. Eventually, we will offer an overview of genetic testing, the ethical considerations, the consequences of incidental findings, and the main indications and limitations of the principal technologies.

BACKGROUND: Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects.
OBJECTIVE: This study develops advanced models to enhance the accuracy of diagnosis of NS.
METHODS: A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians.
RESULTS: All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics.
CONCLUSIONS: Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.

Polydactyly-myopia syndrome is a rare genetic condition characterized by the co-occurrence of polydactyly and myopia. Herein, we present the case of a 28-year-old Muslim male, born of consanguineous parents, who presented with complaints of diminished vision since childhood. Ophthalmologic examination revealed severe myopia with characteristic fundus changes indicative of high myopia. Additionally, the patient exhibited polydactyly in all limbs, with a positive family history of both polydactyly and myopia. This case underscores the importance of recognizing and managing rare syndromes to provide appropriate genetic counseling and clinical care. Further research is warranted to elucidate the underlying genetic mechanisms and optimize therapeutic strategies for polydactyly-myopia syndrome. Awareness of this syndrome among healthcare providers is essential to facilitate early diagnosis and intervention for affected individuals and their families.

Pitt-Hopkins syndrome (PTHS) is a rare genetic disorder resulting from TCF4 gene mutations which is characterized by dysmorphic facial features, psychomotor delay, intellectual disability, breathing anomalies, and seizures. Psychiatric conditions are occasionally seen. We present the case report of a seven-year-old PTHS patient with anxiety, insomnia, and agitation. We discuss the psychopharmacological intervention options for this patient. The present case study reports on a 7-year-old female with PTHS, autism spectrum disorder (ASD), and intellectual disability. She had insomnia, crying spells and agitation complaints. For anxiety symptoms and agitation, risperidone, fluoxetine, and clonazepam treatment were given by the neurologist which caused behavioral disinhibition, paroxysmal agitation and no benefit. After admission to our hospital, aripiprazole and hydroxyzine were prescribed for anxiety and ASD-related irritability. She showed a minimal improvement but hyperventilation attacks were still ongoing. Hydroxyzine was stopped, and quetiapine was given to eliminate sleep disturbance. Her sleep period went up to eleven hours. For the anxiety symptoms, escitalopram was prescribed. She showed improvements in sleep, diminished hyperactivity and decreased frequency of abnormal breathing spells. Also, enhancement of social communication skills like increased eye contact and response to her name was observed. Patients with genetic syndromes may have various psychiatric complaints. Psychopharmacological interventions should be administered carefully for the side effects.

Despite having the same underlying genetic etiology, individuals with the same syndromic form of intellectual developmental disability (IDD) show a large degree of interindividual differences in cognition and IQ. Research indicates that up to 80% of the variation in IQ scores among individuals with syndromic IDDs is attributable to nongenetic effects, including social-environmental factors. In this narrative review, we summarize evidence of the influence that factors related to economic stability (focused on due to its prevalence in existing literature) have on IQ in individuals with syndromic IDDs. We also highlight the pathways through which economic stability is hypothesized to impact cognitive development and drive individual differences in IQ among individuals with syndromic IDDs. We also identify broader social-environmental factors (e.g., social determinants of health) that warrant consideration in future research, but that have not yet been explored in syndromic IDDs. We conclude by making recommendations to address the urgent need for further research into other salient factors associated with heterogeneity in IQ. These recommendations ultimately may shape individual- and community-level interventions and may inform systems-level public policy efforts to promote the cognitive development of and improve the lived experiences of individuals with syndromic IDDs.

UNASSIGNED: A genetic predisposition seems to be involved in biliary tract cancer, but the prevalence of germline mutations in BTC remains unclear, and the therapeutic role of the germline pathologic variants is still unknown.
UNASSIGNED: The aim of the present work is to systematically review the data available on the hereditary predisposition of biliary tract cancer by a specific research on PubMed, in order to highlight the most important critical points and to define the current possible role of germinal testing and genetic counseling in this setting of patients.
UNASSIGNED: Basing on data already available, we decided to start in our institution a specific genetic protocol focused on biliary tract cancer patients, which includes genetic counseling and, if indicated, germline test. The inclusion criteria are: 1) Patient with personal history of oncologic disease other than BTC, 2) Patient with familiar history of oncologic disease (considering relatives of first and second grade), 3) Patient with ≤ 50 years old, 4) Patient presenting a somatic mutation in genes involved in DNA damage repair pathways and mismatch repair. The aim of the presented protocol is to identify germline pathogenic variants with prophylactic and therapeutic impact, and to collect and integrate a significant amount of clinical, familial, somatic, and genetic data.

BACKGROUND: While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism.
OBJECTIVE: We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt.
METHODS: Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity.
RESULTS: DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score\'s levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores.
CONCLUSIONS: If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face.

BACKGROUND: Surgery for Stanford type A aortic dissection (TAAD) is associated with an increased risk of late aortic reoperations due to degeneration of the dissected aorta.
METHODS: The subjects of this analysis were 990 TAAD patients who survived surgery for acute TAAD and had complete data on the diameter and dissection status of all aortic segments.
RESULTS: After a mean follow-up of 4.2 ± 3.6 years, 60 patients underwent 85 distal aortic reoperations. Ten-year cumulative incidence of distal aortic reoperation was 9.6%. Multivariable competing risk analysis showed that the maximum preoperative diameter of the abdominal aorta (SHR 1.041, 95%CI 1.008-1.075), abdominal aorta dissection (SHR 2.133, 95%CI 1.156-3.937) and genetic syndromes (SHR 2.840, 95%CI 1.001-8.060) were independent predictors of distal aortic reoperation. Patients with a maximum diameter of the abdominal aorta >30 mm and/or abdominal aortic dissection had a cumulative incidence of 10-year distal aortic reoperation of 12.0% compared to 5.7% in those without these risk factors (adjusted SHR 2.076, 95%CI 1.062-4.060).
CONCLUSIONS: TAAD patients with genetic syndromes, and increased size and dissection of the abdominal aorta have an increased the risk of distal aortic reoperations. A policy of extensive surgical or hybrid primary aortic repair, completion endovascular procedures for aortic remodeling and tight surveillance may be justified in these patients.
BACKGROUND: ClinicalTrials.gov Identifier: NCT04831073.

Individuals with Kabuki syndrome type 1 (KS1) often have hearing loss recognized in middle childhood. Current clinical dogma suggests that this phenotype is caused by frequent infections due to the immune deficiency in KS1 and/or secondary to structural abnormalities of the ear. To clarify some aspects of hearing loss, we collected information on hearing status from 21 individuals with KS1 and found that individuals have both sensorineural and conductive hearing loss, with the average age of presentation being 7 years. Our data suggest that while ear infections and structural abnormalities contribute to the observed hearing loss, these factors do not explain all loss. Using a KS1 mouse model, we found hearing abnormalities from hearing onset, as indicated by auditory brainstem response measurements. In contrast to mouse and human data for CHARGE syndrome, a disorder possessing overlapping clinical features with KS and a well-known cause of hearing loss and structural inner ear abnormalities, there are no apparent structural abnormalities of the cochlea in KS1 mice. The KS1 mice also display diminished distortion product otoacoustic emission levels, which suggests outer hair cell dysfunction. Combining these findings, our data suggests that KMT2D dysfunction causes sensorineural hearing loss compounded with external factors, such as infection.