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

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

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

Automated Facial Recognition for Noonan Syndrome Using Novel Deep Convolutional Neural Network With Additive Angular Margin Loss

BackgroundNoonan syndrome (NS), a genetically heterogeneous disorder, presents with hypertelorism, ptosis, dysplastic pulmonary valve stenosis, hypertrophic cardiomyopathy, and small stature. Early detection and assessment of NS are crucial to formulating an individualized treatment protocol. However, the diagnostic rate of pediatricians and pediatric cardiologists is limited. To overcome this challenge, we propose an automated facial recognition model to identify NS using a novel deep convolutional neural network (DCNN) with a loss function called additive angular margin loss (ArcFace).MethodsThe proposed automated facial recognition models were trained on dataset that included 127 NS patients, 163 healthy children, and 130 children with several other dysmorphic syndromes. The photo dataset contained only one frontal face image from each participant. A novel DCNN framework with ArcFace loss function (DCNN-Arcface model) was constructed. Two traditional machine learning models and a DCNN model with cross-entropy loss function (DCNN-CE model) were also constructed. Transfer learning and data augmentation were applied in the training process. The identification performance of facial recognition models was assessed by five-fold cross-validation. Comparison of the DCNN-Arcface model to two traditional machine learning models, the DCNN-CE model, and six physicians were performed.ResultsAt distinguishing NS patients from healthy children, the DCNN-Arcface model achieved an accuracy of 0.9201 ± 0.0138 and an area under the receiver operator characteristic curve (AUC) of 0.9797 ± 0.0055. At distinguishing NS patients from children with several other genetic syndromes, it achieved an accuracy of 0.8171 ± 0.0074 and an AUC of 0.9274 ± 0.0062. In both cases, the DCNN-Arcface model outperformed the two traditional machine learning models, the DCNN-CE model, and six physicians.ConclusionThis study shows that the proposed DCNN-Arcface model is a promising way to screen NS patients and can improve the NS diagnosis rate

Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes

Characteristic or classic phenotype of Cornelia de Lange syndrome (CdLS) is associated with a recognisable facial pattern. However, the heterogeneity in causal genes and the presence of overlapping syndromes have made it increasingly difficult to diagnose only by clinical features. DeepGestalt technology, and its app Face2Gene, is having a growing impact on the diagnosis and management of genetic diseases by analysing the features of affected individuals. Here, we performed a phenotypic study on a cohort of 49 individuals harbouring causative variants in known CdLS genes in order to evaluate Face2Gene utility and sensitivity in the clinical diagnosis of CdLS. Based on the profile images of patients, a diagnosis of CdLS was within the top five predicted syndromes for 97.9% of our cases and even listed as first prediction for 83.7%. The age of patients did not seem to affect the prediction accuracy, whereas our results indicate a correlation between the clinical score and affected genes. Furthermore, each gene presents a different pattern recognition that may be used to develop new neural networks with the goal of separating different genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdL

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

Evaluating face2gene as a tool to identify cornelia de lange syndrome by facial phenotypes

Characteristic or classic phenotype of Cornelia de Lange syndrome (CdLS) is associated with a recognisable facial pattern. However, the heterogeneity in causal genes and the presence of overlapping syndromes have made it increasingly difficult to diagnose only by clinical features. DeepGestalt technology, and its app Face2Gene, is having a growing impact on the diagnosis and management of genetic diseases by analysing the features of affected individuals. Here, we performed a phenotypic study on a cohort of 49 individuals harbouring causative variants in known CdLS genes in order to evaluate Face2Gene utility and sensitivity in the clinical diagnosis of CdLS. Based on the profile images of patients, a diagnosis of CdLS was within the top five predicted syndromes for 97.9% of our cases and even listed as first prediction for 83.7%. The age of patients did not seem to affect the prediction accuracy, whereas our results indicate a correlation between the clinical score and affected genes. Furthermore, each gene presents a different pattern recognition that may be used to develop new neural networks with the goal of separating different genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdLS.Spanish Ministry of Science, Innovation and Universities/State Research Agency RTC-2017-6494-1; RTI2018-094434-B-I00 (MCIU/AEI/FEDER, UE) to P.G.-P.; Diputación General de Aragón - FEDER: European Social Fund [Grupo de Referencia B32_17R, to J.P.] as well as funds from the European JPIAMR-VRI network “CONNECT” to P.G.-P.; Medical Faculty of the University of Lübeck J09-2017 to I. P.; German Federal Ministry of Education and Research (BMBF