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Background: Face2Gene (F2G) is a software program, widely used in clinical genetics and dysmorphology for recognizing children with genetic disorders. In order to assess its accuracy in real clinical context, we applied F2G to patients from our genetic counseling center. Results: We randomly selected 151 children, who were referred to genetic counseling because of dysmorphic features and later diagnosed with a particular genetic condition. A frontal photograph was uploaded to the program, and at least three phenotypic traits were selected for each case. Date of birth, sex, weight, height, and head circumference were also entered in the software. The efficacy of the program to correctly diagnose the syndrome based on the facial analysis and/or phenotypic traits was assessed. In 56% (84 cases) of the patients, the F2G program accurately identified the correct diagnosis in its top three suggestions. Forty-seven percent (71 cases) of the patients had the correct diagnosis after applying only facial analysis. There was a statistically significant difference between the two types of analysis—p = 0.001. In 19 of the cases where F2G was unable to identify the correct diagnosis among the top three options based on phenotypic and facial analysis, the diagnosis was included among the 30 suggested syndromes, yielding a total success rate of 68%. The diagnosis was found in the ultra-rare syndromes' suggestions area in six more cases. Conclusions: Our results show that F2G has a good overall performance, but adding phenotypic features to the case under study may increase even further its diagnostic capacity. [ABSTRACT FROM AUTHOR]

Nop Khongthon,1 Midi Theeraviwatwong,1 Khunton Wichajarn,2 Kitiwan Rojnueangnit3 1Medical Students, Faculty of Medicine, Thammasat University, Pathumthani, Thailand; 2Department of Pediatrics, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand; 3Department of Pediatrics, Faculty of Medicine, Thammasat University, Pathumthani, ThailandCorrespondence: Kitiwan Rojnueangnit, Division of Genetics, Department of Pediatrics, Faculty of Medicine, Thammasat University, 99/209 Moo 18 Phahonyothin Road, Klong-Luang, Pathumthani, 12120, Thailand, Tel +66-2-926-9514, Email rkitiwan@tu.ac.thIntroduction: There are more than 6000 genetic syndromes, therefore the recognition of facial patterns may present a challenge for clinicians. The 22q11.2 deletion syndrome (22q11.2 DS) and Williams syndrome (WS) are two different genetic syndromes but share some common phenotypic traits and subtle facial dysmorphisms. Therefore, any tool that would help clinicians recognize genetic syndromes would likely result in a more accurate diagnosis.Methods: The syndrome identification accuracy was compared between 2 different facial analysis algorithms (DeepGestalt and GestaltMatcher) of the Face2Gene (F2G) tool and a group of 9 clinicians with different levels of expertise before and after using F2G for a cohort of 64 Thai participants’ frontal facial photos divided into 3 groups of 22q11.2 DS, WS and unaffected controls.Results: The higher accuracy from the DeepGestalt algorithm than from clinicians was demonstrated, especially when comparing between the two syndromes. The accuracy was highest when clinicians use the tool combined with their own decision-making process. The tool’s second algorithm, GestaltMatcher revealed clear separation among these three groups of photos.Discussion: The result of F2G outperforming clinicians was not surprising. However, the highest increase in accuracy was with nondysmorphology clinicians using F2G.Conclusion: Face2Gene would be a useful tool to help clinicians in facial recognition of genetic syndromes, before ordering specific tests to confirm the definite diagnosis.Keywords: 22q11.2 deletion syndrome, Williams syndrome, accuracy, GestaltMatcher, Face2Gene

Neurodevelopmental disorders exhibit recurrent facial features that can suggest the genetic diagnosis at a glance, but recognizing subtle dysmorphisms is a specialized skill that requires very long training. Face2Gene (FDNA Inc) is an innovative computer‐aided phenotyping tool that analyses patient's portraits and suggests 30 candidate syndromes with similar morphology in a prioritized list. We hypothesized that the software could support even expert physicians in the diagnostic workup of genetic conditions. In this study, we assessed the performance of Face2Gene in an Italian dysmorphological pediatrics clinic. We uploaded two‐dimensional face pictures of 145 children affected by genetic conditions with typical phenotypic traits. All diagnoses were previously confirmed by cytogenetic or molecular tests. Overall, the software's differential included the correct syndrome in most cases (98%). We evaluated the efficiency of the algorithm even considering the rareness of the genetic conditions. All "common" diagnoses were correctly identified, most of them with high diagnostic accuracy (93% in top‐3 matches). Finally, the performance for the most common pediatric syndromes was calculated. Face2Gene performed well even for ultra‐rare genetic conditions (75% within top‐3 matches and 83% within top‐10 matches). Expert geneticists maybe do not need computer support to recognize common syndromes, but our results prove that the tool can be useful not only for general pediatricians but also in dysmorphological clinics for ultra‐rare genetic conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Genetic syndromes often show facial features that provide clues for the diagnosis. However, memorizing these features is a challenging task for clinicians. In the last years, the app Face2Gene proved to be a helpful support for the diagnosis of genetic diseases by analyzing features detected in one or more facial images of affected individuals. Our aim was to evaluate the performance of the app in patients with Silver–Russell syndrome (SRS) and Prader–Willi syndrome (PWS). We enrolled 23 pediatric patients with clinically or genetically diagnosed SRS and 29 pediatric patients with genetically confirmed PWS. One frontal photo of each patient was acquired. Top 1, top 5, and top 10 sensitivities were analyzed. Correlation with the specific genetic diagnosis was investigated. When available, photos of the same patient at different ages were compared. In the SRS group, Face2Gene showed top 1, top 5, and top 10 sensitivities of 39%, 65%, and 91%, respectively. In 41% of patients with genetically confirmed SRS, SRS was the first syndrome suggested, while in clinically diagnosed patients, SRS was suggested as top 1 in 33% of cases (p = 0.74). Face2Gene performed better in younger patients with SRS: in all patients in whom a photo taken at a younger age than the age of enrollment was available, SRS was suggested as top 1, albeit with variable degree of probability. In the PWS group, the top 1, top 5, and top 10 sensitivities were 76%, 97%, and 100%, respectively. PWS was suggested as top 1 in 83% of patients genetically diagnosed with paternal deletion of chromosome 15q11-13 and in 60% of patients presenting with maternal uniparental disomy of chromosome 15 (p = 0.17). The performance was uniform throughout the investigated age range (1–15 years). Conclusion: In addition to a thorough medical history and detailed clinical examination, the Face2Gene app can be a useful tool to support clinicians in identifying children with a potential diagnosis of SRS or PWS. What is Known: • Several genetic syndromes present typical facial features that may provide clues for the diagnosis. • Memorizing all syndromic facial characteristics is a challenging task for clinicians. What is New: • Face2Gene may represent a useful support for pediatricians for the diagnosis of genetic syndromes. • Face2Gene app can be a useful tool to integrate in the diagnostic path of patients with SRS and PWS. [ABSTRACT FROM AUTHOR]

Down syndrome (DS) is typically recognizable in those who present with multiple dysmorphism, especially in regard to facial phenotypes. However, as the presentation of DS in neonates is less obvious, a phenotype‐based presumptive diagnosis is more challenging. Recently, an artificial intelligence (AI) application, Face2Gene, was developed to help physicians recognize specific genetic syndromes by using two‐dimensional facial photos. As of yet, there has not been any study comparing accuracy among physicians or applications. Our objective was to compare the facial recognition accuracy of DS in Thai neonates, using facial photographs, among physicians and the Face2Gene. Sixty‐four Thai neonates at Thammasat University Hospital, with genetic testing and signed parental consent, were divided into a DS group (25) and non‐DS group (39). Non‐DS was further divided into unaffected (19) and those affected with other syndromes (20). Our results revealed physician accuracy (89%) was higher than the Face2Gene (81%); however, the application was higher in sensitivity (100%) than physicians (86%). While this application can serve as a helpful assistant in facilitating any genetic syndrome such as DS, to aid clinicians in recognizing DS facial features in neonates, it is not a replacement for well‐trained doctors. [ABSTRACT FROM AUTHOR]

3q29 deletion syndrome (3q29del) is a recurrent deletion syndrome associated with neuropsychiatric disorders and congenital anomalies. Dysmorphic facial features have been described but not systematically characterized. This study aims to detail the 3q29del craniofacial phenotype and use a machine learning approach to categorize individuals with 3q29del through analysis of 2D photos. Detailed dysmorphology exam and 2D facial photos were ascertained from 31 individuals with 3q29del. Photos were used to train the next‐generation phenotyping algorithm DeepGestalt (Face2Gene by FDNA, Inc, Boston, MA) to distinguish 3q29del cases from controls and all other recognized syndromes. Area under the curve of receiver operating characteristic curves (AUC‐ROC) was used to determine the capacity of Face2Gene to identify 3q29del cases against controls. In this cohort, the most common observed craniofacial features were prominent forehead (48.4%), prominent nose tip (35.5%), and thin upper lip vermillion (25.8%). The FDNA technology showed an ability to distinguish cases from controls with an AUC‐ROC value of 0.873 (p = 0.006) and led to the inclusion of 3q29del as one of the supported syndromes. This study found a recognizable facial pattern in 3q29del, as observed by trained clinical geneticists and next‐generation phenotyping technology. These results expand the potential application of automated technology such as FDNA in identifying rare genetic syndromes, even when facial dysmorphology is subtle. [ABSTRACT FROM AUTHOR]

Xia‐Gibbs syndrome (XGS) is a very rare genetic condition. The clinical spectrum is very broad and variable. The phenotype and evolution in a Congolese boy with XGS have been reported. At 6 years he had speech delay, drooling, marked hyperactivity, attention deficit, aggressive behavior, and intellectual disability. Dysmorphological evaluation revealed strabismus, mild unilateral ptosis, uplifted ear lobes, flat philtrum, thin upper lip vermillion, high arched palate, and flat feet. Patient‐only whole exome sequencing identified a known pathogenic frameshift variant in the AHDC1 gene [NM_001029882.3(AHDC1):c.1122dupC;(p.Gly375ArgfsTer3)]. The clinical follow‐up revealed the deterioration of his fine motor skills and significant cerebellar phenotype including tremor, pes cavus, and gait instability at the age of 12 years. This patient was compared with three previously reported patients with the same variant but did not identify a consistent pattern in the evolution of symptoms with age. [ABSTRACT FROM AUTHOR]

Hyperphosphatasia with mental retardation syndrome (HPMRS) is a rare autosomal recessive disorder caused by pathogenic variants in genes involved in glycosylphosphatidylinositol metabolism that result in a similar phenotype. We describe the first three patients with HPMRS from sub‐Saharan Africa. Detection was assisted by Face2Gene phenotype matching and confirmed by the presence of elevated serum alkaline phosphatase. All three patients had severe intellectual disability, absent speech, hypotonia and palatal abnormality (cleft palate in two, very high‐arched palate in one), no or minimal brachytelephalangy, and high serum alkaline phosphatase levels. Additional findings included seizures in two, and brain imaging abnormalities in two. In all three patients HPMRS was a top‐20 gestalt match using Face2Gene. The overall phenotype is consistent with descriptions in the literature of HPMRS type 4, although not specific to it. Whole exome sequencing in the index patient and his mother detected a candidate variant in a homozygous state in the index patient (PGAP3:c.557G>C, p.Arg186Thr) and heterozygous in the mother. Further variant interpretation indicated pathogenicity. Sanger sequencing of another two patients identified the same homozygous, pathogenic variant, confirming a diagnosis of HPMRS type 4. The shared homozygous variant in apparently unrelated families, and in the absence of consanguinity, suggests the possibility of genetic drift due to a population bottleneck effect, and further research is recommended. [ABSTRACT FROM AUTHOR]

A rare developmental delay (DD)/intellectual disability (ID) syndrome with craniofacial dysmorphisms and autistic features, termed White–Sutton syndrome (WHSUS, MIM#614787), has been recently described, identifying truncating mutations in the chromatin regulator POGZ (KIAA0461, MIM#614787). We describe a further WHSUS patient harboring a novel nonsense de novo POGZ variant, which afflicts a protein domain with transposase activity less frequently impacted by mutational events (DDE domain). This patient displays additional physical and behavioral features, these latter mimicking Smith–Magenis syndrome (SMS, MIM#182290). Considering sleep–wake cycle anomalies and abnormal behavior manifested by this boy, we reinforced the clinical resemblance between WHSUS and SMS, being both chromatinopathies. In addition, using the DeepGestalt technology, we identified a different facial overlap between WHSUS patients with mutations in the DDE domain (Group 1) and individuals harboring variants in other protein domains/regions (Group 2). This report further delineates the clinical and molecular repertoire of the POGZ‐related phenotype, adding a novel patient with uncommon clinical and behavioral features and provides the first computer‐aided facial study of WHSUS patients. [ABSTRACT FROM AUTHOR]

Wiedemann–Steiner syndrome (WWS) is a rare disorder characterized by hypotonia, postnatal growth restriction, striking facial dysmorphism, and hirsutism. It is caused by heterozygous pathogenic variants in KMT2A. This gene has an established role in histone methylation, which explains the overlap of WWS with syndromes caused by genes involved in chromatin remodeling. We describe an infant with a novel single base pair deletion in KMT2A with features consistent with WWS, as well as additional features of stenosis of aqueduct of Sylvius and broad toes. The usefulness of Face2Gene as a tool for identification of dysmorphology syndromes is discussed, as in this patient, it suggested WWS as the top candidate disorder. To the best of our knowledge, this is the first patient of WWS reported from India, with a novel genotype and expanded phenotype. [ABSTRACT FROM AUTHOR]

The pathogenic variants in the neuroblastoma‐amplified sequence (NBAS) are associated with a clinical spectrum involving the hepatic, skeletal, ocular, and immune systems. Here, we report on two unrelated subjects with a complex phenotype solved by whole‐exome sequencing, who shared a synonymous change in NBAS that was documented to affect the transcript processing and co‐occurring with a truncating change. Starting from these two cases, we systematically assessed the clinical information available for all subjects with biallelic NBAS pathogenic variants (73 cases in total). We revealed a recognizable facial profile (hypotelorism, thin lips, pointed chin, and "progeroid" appearance) determined by using DeepGestalt facial recognition technology, and we provide evidence for the occurrence of genotype–phenotype correlations. Notably, severe hepatic involvement was associated with variants affecting the NBAS‐Nter and Sec39 domains, whereas milder liver involvement and immunodeficiency were generally associated with variants located at the N‐terminus and C‐terminus of the protein. Remarkably, no patient was reported to carry two nonsense variants, suggesting lethality of complete NBAS loss‐of‐function. [ABSTRACT FROM AUTHOR]

Craniofacial dysmorphism recognition is the first step in diagnosing most genetic syndromes. However, the number of genetic syndromes is enormous, and the specific facial features are difficult to memorize. For clinical practice, recent advances in artificial intelligence can be of use. One such tool, Face2Gene (FDNA, Inc., Boston, MA), is an innovative free group of applications, that helps clinicians recognize possible genetic syndromes from patients’ facial two‐dimensional photos. The initial data set used to train this technology consisted primarily of Caucasian patients. Because ethnic differences affect patients’ facial features, the recognition probability in Asian patients may be limited. Our aim was to test the technology's recognition probability on Thai children with Down Syndrome (DS) as compared to Thai children without DS (non‐DS). Two separate control groups of Thai non‐DS children, either unaffected or having other syndromes, were included. Frontal photographs were obtained from all the participants. All 30 children with DS were recognized as DS in the top 10 syndrome‐matches (100% sensitivity), and 27 were in the first ranking of suggested syndromes. Eighteen non‐DS were recognized as DS (87.2% specificity) with an accuracy of 89%. We present a scientific basis for this novel tool, useful in the clinic where patients are of a different ethnicity unfamiliar to the evaluator. However, Face2Gene cannot be considered a replacement for clinicians’ knowledge of phenotypes. Further studies on other genetic syndromes/ethnicities being identified by software algorithms are needed. [ABSTRACT FROM AUTHOR]

Computer-aided software such as the facial image diagnostic aid (FIDA) and Face2Gene has been developed to perform pattern recognition of facial features with promising clinical results. The aim of this pilot study was to test Face2Gene's recognition performance on Bantu Congolese subjects with Fragile X syndrome (FXS) as compared to Congolese subjects with intellectual disability but without FXS (non‐FXS). Frontal facial photograph from 156 participants (14 patients with FXS and 142 controls) predominantly young-adults to adults, median age 18.9 age range 4–39yo, were uploaded. Automated face analysis was conducted by using the technology used in proprietary software tools called Face2Gene CLINIC and Face2Gene RESEARCH (version 17.6.2). To estimate the statistical power of the Face2Gene technology in distinguishing affected individuals from controls, a cross validation scheme was used. The similarity seen in the upper facial region (of males and females) is greater than the similarity seen in other parts of the face. Binary comparison of subjects with FXS versus non-FXS and subjects with FXS versus subjects with Down syndrome reveal an area under the curve values of 0.955 (p = 0.002) and 0.986 (p = 0.003). The Face2Gene algorithm is separating well between FXS and Non-FXS subjects. [ABSTRACT FROM AUTHOR]

High throughput approaches are continuously progressing and have become a major part of clinical diagnostics. Still, the critical process of detailed phenotyping and gathering clinical information has not changed much in the last decades. Forms of next generation phenotyping (NGP) are needed to increase further the value of any kind of genetic approaches, including timely consideration of (molecular) cytogenetics during the diagnostic quest. As NGP we used in this study the facial dysmorphology novel analysis (FDNA) technology to automatically identify facial phenotypes associated with Emanuel (ES) and Pallister‐Killian Syndrome (PKS) from 2D facial photos. The comparison between ES or PKS and normal individuals expressed a full separation between the cohorts. Our results show that NPG is able to help in the clinic, and could reduce the time patients spend in diagnostic odyssey. It also helps to differentiate ES or PKS from each other and other patients with small supernumerary marker chromosomes, especially in countries with no access to more sophisticated genetic approaches apart from banding cytogenetics. Inclusion of more facial pictures of patient with sSMC, like isochromosome‐18p‐, cat‐eye‐syndrome or others may contribute to higher detection rates in future. [ABSTRACT FROM AUTHOR]

The evaluation of facial dysmorphism is a critical step toward reaching a diagnostic. The aim of the present study was to evaluate the ability to interpret facial morphology in African children with intellectual disability ( ID). First, 10 experienced clinicians (five from Africa and five from Europe) rated gestalt in 127 African non-Down Syndrome (non- DS) patients using either the score 2 for 'clearly dysmorphic', 0 for 'clearly non dysmorphic' or 1 for 'uncertain'. The inter-rater agreement was determined using kappa coefficient. There was only fair agreement between African and European raters ( kappa-coefficient = 0.29). Second, we applied the FDNA Face2Gene solution to assess Down Syndrome ( DS) faces. Initially, Face2Gene showed a better recognition rate for DS in Caucasian (80%) compared to African (36.8%). We trained the Face2Gene with a set of African DS and non- DS photographs. Interestingly, the recognition in African increased to 94.7%. Thus, training improved the sensitivity of Face2Gene. Our data suggest that human based evaluation is influenced by ethnic background of the evaluator. In addition, computer based evaluation indicates that the ethnic of the patient also influences the evaluation and that training may increase the detection specificity for a particular ethnic. [ABSTRACT FROM AUTHOR]

Intellectual developmental disorder, autosomal dominant 7 (MRD7; OMIM 614104) is a rare disease characterized by microcephaly, intellectual disability, speech delay, feeding difficulties, and facial dysmorphisms. This disorder is caused by pathogenic/likely pathogenic variants of the DYRK1A gene, which encodes dual-specificity tyrosine-phosphorylation-regulated kinase 1A. Here, we report a case of MRD7 that was diagnosed using Face2Gene and whole-exome sequencing (WES). A 22-year-old man presented with microcephaly, intellectual disability, slender body, long slender fingers, and facial dysmorphisms. He was previously diagnosed with Cornelia de Lange syndrome (CdLS) at four years of age. However, his CdLS clinical diagnostic score was low at 22 years of age. The Face2Gene application introduced several candidate diseases including MRD7. Finally, by utilizing WES and Sanger sequencing analysis of cloned cDNA, we identified a novel heterozygous duplication variant (c.848dup, p.(Asn283LysfsTer6)) in the DYRK1A gene, which introduces a premature stop codon. This report provides more information about the phenotypic spectrum of a young adult patient with MRD7. Face2Gene helped us introduce candidate diseases of the patient. Registering further genetically confirmed cases with MRD7 will improve the accuracy of the diagnostic recommendations in Face2Gene. Moreover, WES is a powerful tool for diagnosing rare genetic diseases, such as MRD7., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Obara et al.)

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. [ABSTRACT FROM AUTHOR]

Mucolipidosis type IV (ML-IV) is a rare autosomal-recessive lysosomal storage disease, caused by mutations in MCOLN1. ML-IV manifests with developmental delay, esotropia and corneal clouding. While the clinical phenotype is well-described, the diagnosis of ML-IV is often challenging and elusive. Our experience with ML-IV patients brought to the clinical observation that they share common and identifiable facial features, not yet described in the literature to date. Here, we utilized a computerized facial analysis tool to establish this association. Using the DeepGestalt algorithm, 50 two-dimensional facial images of ten ML-IV patients were analyzed, and compared to unaffected controls (n = 98) and to individuals affected with other genetic disorders (n = 99). Results were expressed in terms of the area-under-the-curve (AUC) of the receiver-operating-characteristic curve (ROC). When compared to unaffected cases and to cases diagnosed with syndromes other than ML-IV, the ML-IV cohort showed an AUC of 0.822 (p value < 0.01) and an AUC of 0.885 (p value < 0.001), respectively. We describe recognizable facial features typical in patients with ML-IV. Reaffirmed by the DeepGestalt technology, the described common facial phenotype adds to the tools currently available for clinicians and may thus assist in reaching an earlier diagnosis of this rare and underdiagnosed disorder. [ABSTRACT FROM AUTHOR]

Pitt Hopkins syndrome (PTHS) is a very rare condition and until now, approximately 500 patients were reported worldwide, of which not all are genetically confirmed. Usually, individuals with variants affecting exons 1 to 5 in the TCF4 gene associate mild intellectual disability (ID), between exons 5 to 8, moderate to severe ID and sometimes have some of the characteristics of PTHS, and variants starting from exon 9 to exon 20 associate a typical PTHS phenotype. In this report, we describe the clinical and molecular findings of a Caucasian boy diagnosed with PTHS. PTHS phenotype is described including craniofacial dysmorphism with brachycephaly, biparietal narrowing, wide nasal bridge, thin and linear lateral eyebrows, palpebral edema, full cheeks, short philtrum, wide mouth with prominent and everted lips, prominent Cupid's bow, downturned corners of the mouth, microdontia and also the clinical management of the patient. The previously and the current diagnosis scores are described in this report and also the challenges and their benefits for an accurate and early diagnosis. [ABSTRACT FROM AUTHOR]

Osteopathia striata with cranial sclerosis is an X-linked dominant bone dysplasia with osteosclerosis. It should be suspected in girls with macrocephaly, intellectual disability with unique facial dysmorphic features. We described the clinical and radiological profile of a patient with this rare disorder. A novel heterozygous variant was identified in the AMER1 gene which leads to premature truncation of the AMER1 protein. Facial gestalt recognition using artificial intelligence and radiographic features were used to narrow the differential diagnosis., Competing Interests: Conflict of Interest None declared., (Thieme. All rights reserved.)

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. [ABSTRACT FROM AUTHOR]