The Limited Reign of Saturn\u27s Rings

Saturn’s rings—stretching tens of thousands of miles above its equator but no more than a few hundred yards thick—mark an ancient debris field of orbiting ice shards, the remains of a moon-sized object that strayed too close and was torn to pieces by Saturn’s intense gravitation. Astronomers have debated when the rings formed and how long they will stay in orbit. Recent observations from large, land-based telescopes and orbiting spacecraft reveal that Saturn’s rings are remarkably young and are dissipating at a rapid rate. [excerpt

PEDIA: prioritization of exome data by image analysis.

PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

PEDIA: prioritization of exome data by image analysis

Purpose Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

The Connective Tissue Disorder Associated with Recessive Variants in the SLC39A13 Zinc Transporter Gene (Spondylo-Dysplastic Ehlers-Danlos Syndrome Type 3): Insights from Four Novel Patients and Follow-Up on Two Original Cases.

Recessive loss-of-function variants in SLC39A13, a putative zinc transporter gene, were first associated with a connective tissue disorder that is now called "Ehlers-Danlos syndrome, spondylodysplastic form type 3" (SCD-EDS, OMIM 612350) in 2008. Nine individuals have been described. We describe here four additional affected individuals from three consanguineous families and the follow up of two of the original cases. In our series, cardinal findings included thin and finely wrinkled skin of the hands and feet, characteristic facial features with downslanting palpebral fissures, mild hypertelorism, prominent eyes with a paucity of periorbital fat, blueish sclerae, microdontia, or oligodontia, and-in contrast to most types of Ehlers-Danlos syndrome-significant short stature of childhood onset. Mild radiographic changes were observed, among which platyspondyly is a useful diagnostic feature. Two of our patients developed severe keratoconus, and two suffered from cerebrovascular accidents in their twenties, suggesting that there may be a vascular component to this condition. All patients tested had a significantly reduced ratio of the two collagen-derived crosslink derivates, pyridinoline-to-deoxypyridinoline, in urine, suggesting that this simple test is diagnostically useful. Additionally, analysis of the facial features of affected individuals by DeepGestalt technology confirmed their specificity and may be sufficient to suggest the diagnosis directly. Given that the clinical presentation in childhood consists mainly of short stature and characteristic facial features, the differential diagnosis is not necessarily that of a connective tissue disorder and therefore, we propose that SLC39A13 is included in gene panels designed to address dysmorphism and short stature. This approach may result in more efficient diagnosis

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Background: Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective: The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods: Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results: We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions: DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools

Evaluation der diagnostischen Genauigkeit eines Systems zur computergestützten fazialen Phänotypisierung syndromaler Patientinnen und Patienten

In der Syndromologie ist die computergestützte Gesichtsanalyse von Patient*innen mit fazialen Dysmorphien zu einem bedeutenden Instrument in der Diagnostik genetisch-syndromaler Erkrankungen geworden. Durch maschinelles Lernen werden Softwareprogramme wie Face2Gene, Clinical Face Phenotype Space und FaceBase in der Erkennung dysmorpher Gesichtszüge durch automatisierte Bildanalyse trainiert. Abhängig von der Übereinstimmung zwischen dem Bild einer Person und dem System zugrunde liegenden Bildern anderer Betroffener wird eine Liste von Differentialdiagnosen präsentiert. Angesichts der hohen Kosten genetischer Analysen und der Seltenheit einzelner genetischer Syndrome kann die automatisierte Bildanalyse Kliniker*innen helfen, eine diagnostische Odyssee zu verkürzen. Face2Gene ist allerdings so angelegt, dass jedem Bild eine Liste von Differentialdiagnosen zugeordnet wird. Bilder von fazial unauffälligen Personen können also nicht als solche erkannt werden. Diese Studie prüft 1) Face2Gene’s Sensitivität, 2) ob sich die Gestalt Scores für syndromale Gesichter von denen für unauffällige Gesichter signifikant unterscheiden und 3) wie sich die vorgeschlagenen Differentialdiagnosen innerhalb der gesunden Kontrollkohorte verteilen (Spezifität des Systems) und 4) ob der ethnische Hintergrund bzw. das Geschlecht Face2Gene’s diagnostische Genauigkeit beeinflussen.In syndromology, computer-aided facial analysis of patients with facial dysmorphisms has become a significant tool in the diagnosis of genetic syndromic disorders. Through machine learning, software such as Face2Gene, Clinical Face Phenotype Space, and FaceBase is trained in the detection of facial dysmorphic features through automated image analysis. Based on comparison to previous images, a list of differential diagnoses is presented. Given the high cost of genetic analysis and the rarity of individual genetic syndromes, automated image analysis can help clinicians shorten a diagnostic odyssey. However, images of facially inconspicuous individuals cannot be identified as such. This study tests 1) Face2Gene's sensitivity, 2) whether Gestalt scores of syndromic faces differ significantly from those of inconspicuous faces, and 3) how the suggested differential diagnoses are distributed within the healthy control cohort (specificity of the system), and 4) whether ethnic background or gender affect Face2Gene's diagnostic accuracy

Artificial intelligence (AI) in rare diseases: is the future brighter?

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.info:eu-repo/semantics/publishedVersio

Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders

Dissertation (MSc (Genetics))--University of Pretoria, 2022.Congenital disorders are a major health care burden. Most congenital disorders that are due to genetic causes do not have a cure, but an early and accurate diagnosis may alleviate associated symptoms and contribute to the correct management of the disorder. However, there is a lack of medical geneticists and doctors who can make these diagnoses in developing countries. Thus, facial analysis technologies can provide a quick and objective way to initially diagnose individuals with a congenital disorder where resources are limited because almost half of all inherited disorders have a typical facial gestalt. Chapter 1 is a literature review, focusing on facial analysis technologies and how it is used to make an initial diagnosis based on the typical facial features of an individual, with a special focus on Face2Gene. I briefly reviewed the four disorders under investigation in this study, their prevalence, cause, and particularly the typical facial features associated with each disorder. We first aimed to better understand parents’ views on the collection, storage, use, and publication of their children’s facial images for research and diagnosis. Large datasets of facial photographs are required to train facial analysis algorithms, and we wanted to better understand the public’s views on this topic. This was achieved by conducting an online survey, found in Chapter 2, aimed at parents of children with and without a congenital disorder. The second aim of this study was to determine and compare the diagnostic accuracies of two- dimensional facial analyses of congenital disorders. Face2Gene is a popular phenotyping web tool and is free to use for healthcare professionals. The technology does not, however, classify an individual as “non-syndromic” and will suggest likely syndromes to all submitted facial images. Differentiation between syndromic and non-syndromic individuals is important for clinicians to determine if the child requires further testing or investigation into a potential diagnosis. Chapter 3 aimed to establish how well Face2Gene can differentiate between syndromic and non-syndromic facial images, and we compared that to our in-house analyses of the facial features of individuals. Previous research showed that Face2Gene did not perform well in African ethnic groups before training. This is likely due to the algorithm’s training data mostly consisting of European individuals. It is also important to establish a diagnosis as early as possible, to ensure the correct management strategies are put in place. In Chapter 4, we thus aimed to establish how well the Face2Gene algorithm can differentiate between syndromic and non-syndromic facial images in different syndrome, ethnic, and age groups. We again compared that to the results from our in-house analyses.University of PretoriaBiochemistry, Genetics and Microbiology (BGM)MSc (Genetics)Unrestricte

Utilidad diagnostica de face2gene en síndrome de Cornelia de lange de casos publicados en pubmed (2017-2022) en el periodo de julio-septiembre 2022.

Face2gene se basa en el análisis dismorfologíco facial y fenotipo clínico para brindar 30 resultados diagnósticos probables en orden de mayor a menor coincidencia de rasgos faciales, esta muestra los genes que se pueden identificar en cada síndrome permitiendo reducir la incerteza diagnostica en cuanto a enfermedades raras. Se decide comprobar la utilidad diagnostica con el Síndrome de Cornelia de Lange caracterizado por cambios faciales, alteraciones en crecimiento, desarrollo e inteligencia entre otros. Se revisan 102 casos de síndrome de Cornelia de Lange alojados en PubMed de los cuales 33 fueron incluidos en el estudio ya que cumplieron los criterios de inclusión y exclusión, además se suman 30 casos de enfermedades raras diferenciales como Síndrome de Coffin Siris, síndrome de Wiedemann Steiner, síndrome de Rubinstein Taybi, síndrome de CHOPS, síndrome KBG y otros síndromes no diferenciales. Se determinan los parámetros de la prueba para comprobar la utilidad de Face2Gene como herramienta de orientación diagnostica los cuales evidencian una sensibilidad de 96.97%, especificidad de 80%, el valor predictivo negativo fue de 96%, Falsos negativos solamente se identifican en un 3% lo que es un excelente valor y 20% de falsos positivos lo que es un buen porcentaje a pesar de que los resultados en el análisis de cada caso pueden tener 30 posibilidades, los genes propuestos por la herramienta coinciden con los que se encontraron en los casos como prueba diagnóstica, el único gen que no proponía era HDAC2, el cual es una nueva variante asociada a la enfermedad