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Computer-aided Facial Analysis in Diagnosing Dysmorphic Syndromes in Indian Children
Abstract
Objective:
To assess the utility of computer-aided facial analysis in identifying dysmorphic syndromes in Indian children.
Methods:
Fifty-one patients with a definite molecular or cytogenetic diagnosis and recognizable facial dysmorphism were enrolled in the study and their facial photographs were uploaded in the Face2Gene software. The results provided by the software were compared with the molecular diagnosis.
Results:
Of the 51 patients, the software predicted the correct diagnosis in 37 patients (72.5%); predicted as the first in the top ten suggestions in 26 (70.2%). In 14 patients, the software did not suggest a correct diagnosis.
Conclusions:
Computer-aided facial analysis is a method that can aid in diagnosis of genetic syndromes in Indian children. As more clinicians start to use this software, its accuracy is expected to improve.
... The term "biomarker" refers to any biological marker that may be measured and utilized as an indicator of the presence or absence of a condition, such as a disease [42]. The levels of beta-amyloid and proteins called tau in the Cerebrospinal fluid are one of the first indicators used to identify Alzheimer's disease [43]. This is because the key alterations in Alzheimer's patients are beta-amyloid acid deposition and tau protein accumulation in the brain. ...
... The findings further suggest that patients are affected by their surroundings. Narayanan et al. [43] utilized online Face2Gene software to detect dysmorphic disorders. They employed 51 patient facials were examined using facial analysis techniques. ...
Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer’s disease. Alzheimer’s disease research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person’s mental health. Support vector machine is the first technique. Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several support vector machines kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network architecture to identify Alzheimer’s-related mental disorders. According to the findings, the support vector machines approach accurately recognized over 93% of the photos tested. The deep convolutional neural network approach was one hundred percent accurate during model training, whereas the support vector machines approach achieved just 93 percent accuracy. In contrast to support vector machines accuracy of 89.3%, the deep convolutional neural network model test findings were accurate 98.8% of the time. Based on the findings reported here, the proposed deep convolutional neural network architecture may be used for diagnostic purposes involving the patient’s mental state.
... This did not improve after adding clinical features (among the possible features listed in the app, only short stature was applicable to both patients). For these 2 patients with clinical SRS, the diagnosis was not genetically confirmed after extensive genetic investigations (methylation and CNV analysis chromosome of 6,7,11,14,15,19, and 20 and next-generation sequencing of 18 short stature-related genes including HMGA2). Both patients fulfilled the clinical diagnosis of SRS based on 4 out of 6 criteria from the Netchine-Harbison clinical scoring system ((1) small for gestational age, (2) postnatal growth retardation, (3) severe feeding difficulties in early life with a BMI ≤ − 2 SDS, and (4) a protruding forehead at toddler age). ...
... Similar results were found by Marwaha et al. (top 10 sensitivity 57%, increasing to 82% when patients with syndromes not included by Face2Gene were excluded). The app has been validated in non-Caucasian patients, showing a good ability in suggesting the diagnosis despite facial variations that occur in different ethnic groups [18][19][20]. ...
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.
... [30][31][32][33] The term "biomarker" refers to any biological marker that may be measured and utilized as an indicator of the presence or absence of a condition, such as a disease [34]. The levels of beta-amyloid and proteins called tau in the CSF uid are one of the rst indicators used to identify Alzheimer's disease [35]. This is because the key alterations in Alzheimer's patients are beta-amyloid acid deposition and tau protein accumulation in the brain. ...
... Narayanan et al. [35] utilized online Face2Gene software to detect dysmorphic disorders. They employed 51 patient facials were examined using facial analysis techniques. ...
Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer's disease. Ad research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person's mental health. Support vector machine is the first technique (SVM). Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several SVM kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network (DCNN) architecture to identify Alzheimer's-related mental disorders. According to the findings, the SVM approach accurately recognized over 93% of the photos tested. The DCNN approach was one hundred percent accurate during model training, whereas the SVM approach achieved just 93 percent accuracy. In contrast to SVM's accuracy of 89.3%, the DCNN model test's findings were accurate 98.8% of the time. Based on the findings reported here, the proposed DCNN architecture may be used for diagnostic purposes involving the patient's mental state.
... AI can achieve a clinical diagnosis now, without the help of any laboratory or imaging modality. Narayanan, et al. [5] have attempted just that through their study published in this issue of Indian Pediatrics. The study involved testing the software to make an accurate diagnosis in 51 previously confirmed cases of dysmorphic genetic syndromes. ...
... The study by Narayanan, et al. [5] is the first of its kind from India, which paves a path for use of this handy software in the clinics. The diagnostic accuracy in this cohort is encouraging. ...
... Zarate et al. (2019) [4], performed subsequent research, concluding that combined data from both studies showed a top 10 sensitivity rate of 86.6% (52/60) in the routine clinical setting for conditions with a validated facial model, exclusively based on facial analysis. After, other authors tested this tool in the routine clinical setting, also in individuals of Asiatic descent, showing the tool's reliability exclusively based on facial analysis [5,6]. ...
Background
In this study, we used the novel DeepGestalt technology powered by Face2Gene (FDNA Inc., MA, USA) in suggesting a correct diagnosis based on the facial gestalt of well-known multiple anomaly syndromes. Only molecularly characterized pediatric patients were considered in the present research.
Subjects and methods
A total of 19 two-dimensional (2D) images of patients affected by several molecularly confirmed craniofacial syndromes (14 monogenic disorders and 5 chromosome diseases) and evaluated at the main involved Institution were analyzed using the Face2Gene CLINIC application (vs.19.1.3). Patients were cataloged into two main analysis groups (A, B) according to the number of clinical evaluations. Specifically, group A contained the patients evaluated more than one time, while in group B were comprised the subjects with a single clinical assesment. The algorithm’s reliability was measured based on its capacity to identify the correct diagnosis as top-1 match, within the top-10 match and top-30 matches, only based on the uploaded image and not any other clinical finding or HPO terms. Failure was represented by the top-0 match.
Results
The correct diagnosis was suggested respectively in 100% (8/8) and 81% (9/11) of cases of group A and B, globally failing in 16% (3/19).
Conclusion
The tested tool resulted to be useful in identifying the facial gestalt of a heterogeneous group of syndromic disorders.
This study illustrates the first Italian experience with the next generation phenotyping technology, following previous works and providing additional observations.
... Furthermore, the DeepGestalt technology has been demonstrated to not be influenced by ethnicity, according to previously published works, which analyzed the facial features of individuals with different ancestries and affected by diverse genetic conditions [15][16][17] . These studies showed that the technology identifies facial gestalt independently from an individual' s ancestry. ...
Aim: This is the first computer-assisted study focused on the craniofacial features of the intellectual disability (ID)/developmental delay (DD) syndrome related to haploinsufficiency of the SETD5 gene (SET domain-containing protein 5, MIM#615743), which is a chromatin regulator. The purpose of this novel research is to better delineate the facial phenotype of this condition and identify the associated dysmorphic features to consider for clinical diagnosis.
Methods: A total of 18 2D frontal images of previously published pediatric individuals (aged 1-14 years, Caucasian ethnicity) with SETD5 mutations (SETD5, cohort 1) were uploaded to the RESEARCH application of the Face2Gene online platform (V.19.1.3) (FDNA Inc., Boston, MA, USA). Images from this group of patients were compared with 36 photos of individuals with two other known chromatin disorders, specifically KBG (KBGS, cohort 2, 18 images) and Koolen-de Vries syndromes (KdVS, cohort 3, 18 images), which share with the SETD5-related ID syndrome a very similar facial gestalt and peculiar dysmorphisms. An additional cohort of 18 unaffected controls that were matched for age and ethnicity (Ctrl., controls, cohort 4) was also included in the comparison experiment.
Results: Results obtained from the binary comparison analysis were expressed in terms of Area Under the Curve and its Receiver Operating Characteristic curve for aggregated splits. A high facial overlap between the SETD5-related phenotype and KBGS was demonstrated. Other conditions considered for the study were well recognized by the system and differentiated using the unaffected controls.
Conclusion: This study confirms the presence of distinctive dysmorphic features that characterize the SETD5-related facial phenotype, providing observations about its possible role in facial morphogenesis.
... The first consists of technical studies that focus on the applications and capabilities of facial processing. This covers issues such as classification of faces based on regional affiliations -North and South Indian (Katti and Arun, 2019) or North, East and South Indian (Sarin and Panda, 2020), identification of genetic disorders in children (Narayanan et al., 2019), and detection of emotions (Singh and Benedict, 2020). The second stream of work consists of research papers, reports and other critical perspectives on the use of facial processing, highlighting associated risks, harms, and modes of regulation (Bhandari, 2021;Joshi, 2020;Aneja and Chamuah, 2020;Kovacs, 2020;Parsheera, 2019;Marda, 2019a;Basu and Sonkar, 2019). ...
The increasing adoption of facial processing systems in India is fraught with concerns of privacy, transparency, accountability, and missing procedural safeguards. At the same time, we also know very little about how these technologies perform on the diverse features, characteristics, and skin tones of India's 1.34 billion-plus population. In this paper, we test the face detection and facial analysis functions of four commercial facial processing tools on a dataset of Indian faces. The tools display varying error rates in the face detection and gender and age classification functions. The gender classification error rate for Indian female faces is consistently higher compared to that of males -- the highest female error rate being 14.68%. In some cases, this error rate is much higher than that shown by previous studies for females of other nationalities. Age classification errors are also high. Despite taking into account an acceptable error margin of plus or minus 10 years from a person's actual age, age prediction failures are in the range of 14.3% to 42.2%. These findings point to the limited accuracy of facial processing tools, particularly for certain demographic groups, and the need for more critical thinking before adopting such systems.
... In addition to CdLS, cases of alcohol related neurodevelopmental disorder were identified by the system more efficiently than by manual methods [12]. Face2Gene has been used on an international scale in the United States, Canada, Japan and India, for example [13][14][15][16]. This is important to note since dysmorphologists have noted that some physical characteristics vary based on ethnicity, . ...
Genetic variants in the gene Ankyrin Repeat Domain 11 (ANKRD11) and deletions in 16q24.3 are known to cause KBG syndrome, a rare syndrome associated with craniofacial, intellectual, and neurobehavioral anomalies. We report 25 unpublished individuals from 22 families, all with molecularly confirmed diagnoses of KBG syndrome. Twenty-one individuals have de novo variants, three have inherited variants, and one is inherited from a mother exhibiting low-level mosaicism. Of these variants, 20 are truncating (frameshift or nonsense), and five are missense. We created a novel protocol for collection and reporting of data, including prospectively interviewing these individuals and their families throughout eight countries via videoconferencing by a single clinician. Participants' medical records, including imaging, were reviewed, and data was uploaded to the Human Disease Gene website using Human Phenotype Ontology (HPO) terms. Photos of the participants were submitted to GestaltMatcher and Face2Gene (FDNA Inc, USA) for facial analysis, and we found similar facial phenotypes among the participants. Within our cohort, common traits included short stature, macrodontia, anteverted nares, wide nasal bridge, wide nasal base, thick eyebrows, synophrys and hypertelorism. Seventy-two percent of participants had gastrointestinal complaints and 80% had hearing loss. Three participants were started on growth hormone with positive results. Behavioral issues and global developmental delays were found in most participants. Neurologic abnormalities including seizures and/or EEG abnormalities were also very common (44%), suggesting that early detection and seizure prophylaxis could be an important point of intervention. Twenty-four percent were diagnosed with attention deficit hyperactivity disorder (ADHD) and 28% were diagnosed with autism spectrum disorder (ASD). Additionally, we have identified minimally reported symptoms, including recurrent sinus infections (16%) and previously unreported migraines (20%). Based on the videoconferencing and these data, we provide a set of recommendations regarding diagnostic and treatment approaches for KBG syndrome.
... This application suggests Robinow syndrome as a differential diagnosis based on gestalt and phenotypic features for these previously published cases as shown in Fig. 2. For the present case, Robinow syndrome was fifth differential diagnosis on evaluation by Face2Gene. Use of such computational tools aids in the diagnosis of rare genetic syndromes with positive results reported in up to 72.5% cases [10]. The whole-exome sequencing did not reveal any pathogenic or likely pathogenic variations in any of the genes known to cause Robinow syndrome, namely ROR2, DVL1, DVL3, WNT5A, FZD2, NXN, RAC3, or GPC4. ...
Genetic disorders can be monogenic or chromosomal. Deletions, duplications, and cryptic imbalances due to rearrangements of the telomeres are seen in a number of patients with psychomotor and language delay. Here, the authors report a case of 1-y-old boy born to nonconsanguineous couple who was evaluated for global developmental delay with phenotypic resemblance to a monogenic disorder namely Robinow syndrome. Cytogenetic microarray showed a double segment imbalance involving chromosome 6p25.3p25.2 and chromosome 8q23.3q24.3. Robinow syndrome also known as fetal face syndrome is a rare disorder with characteristic facial phenotype resembling fetal face with macrocephaly, low-set ears, broad great toes, gum hypertrophy, micropenis, and rhizomelia. Facial features include hypertelorism, wide mouth and short nose with upturned tip. It can have dominant or recessive mode of inheritance. The chromosomal abnormality in this case may provide clue to some novel gene for Robinow syndrome etiology.
... Studies of more recent versions of DeepGestalt suggested that ethnicity had no major influence on its sensitivity [26,29]. In our set of syndromic images, DeepGestalt's sensitivity is remarkably high, which is in line with the previous studies highlighting DeepGestalt's good general sensitivity [11,36,42]. This high sensitivity of DeepGestalt was confirmed for both groups of images, those of White persons and those of persons of other ethnicities. ...
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.
... [13][14][15][16][17][18][19][20] In recent years, artificial intelligence has been developed for the automated and accurate identification of various genetic diseases with facial phenotypes using 2-dimensional or 3-dimensional facial images. [5,9,[21][22][23][24][25] However, the diagnostic performance of different algorithms base on artificial intelligence to identify genetic diseases with facial phenotypes requires further investigation. A meta-analysis of diagnostic performance represents a powerful method to summarize findings in the publications by considering and enabling synthesis of differences between various studies. ...
Background:
Many genetic diseases are known to have distinctive facial phenotypes, which are highly informative to provide an opportunity for automated detection. However, the diagnostic performance of artificial intelligence to identify genetic diseases with facial phenotypes requires further investigation. The objectives of this systematic review and meta-analysis are to evaluate the diagnostic accuracy of artificial intelligence to identify the genetic diseases with face phenotypes and then find the best algorithm.
Methods:
The systematic review will be conducted in accordance with the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols" guidelines. The following electronic databases will be searched: PubMed, Web of Science, IEEE, Ovid, Cochrane Library, EMBASE and China National Knowledge Infrastructure. Two reviewers will screen and select the titles and abstracts of the studies retrieved independently during the database searches and perform full-text reviews and extract available data. The main outcome measures include diagnostic accuracy, as defined by accuracy, recall, specificity, and precision. The descriptive forest plot and summary receiver operating characteristic curves will be used to represent the performance of diagnostic tests. Subgroup analysis will be performed for different algorithms aided diagnosis tests. The quality of study characteristics and methodology will be assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data will be synthesized by RevMan 5.3 and Meta-disc 1.4 software.
Results:
The findings of this systematic review and meta-analysis will be disseminated in a relevant peer-reviewed journal and academic presentations.
Conclusion:
To our knowledge, there have not been any systematic review or meta-analysis relating to diagnosis performance of artificial intelligence in identifying the genetic diseases with face phenotypes. The findings would provide evidence to formulate a comprehensive understanding of applications using artificial intelligence in identifying the genetic diseases with face phenotypes and add considerable value in the future of precision medicine.
Osf registration:
DOI 10.17605/OSF.IO/P9KUH.
Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics.
This integrative review synthesizes research findings from 2008 to 2020 on facial recognition software deployed for young adult and adolescent populations. The aim is to determine the extent to which tests deem these technologies effective, and the extent to which test design considers potential human factors and inherent ethical issues. The review answers the following questions: How are such applications tested? What are the strengths and weaknesses of test design? And what human factors issues do the tests address or implicate? Facial recognition software for this group primarily used experimental design but failed to meet sampling standards necessary for validating and generalizing findings. The software tested, study design, and topics covered left lingering questions about the potential clinical and ethical applications of the technology. They also overwhelmingly did not address the complexities of facial change over time and ethnicities that confound the accuracy of facial recognition software. Facial recognition bodes promising, but human factors could improve their development.
Computer‐assisted pattern recognition platforms, such as Face2Gene® (F2G), can facilitate the diagnosis of children with rare genetic syndromes by comparing a patient's features to known genetic diagnoses. Our work designed, implemented, and evaluated an innovative model of care in clinical genetics in a heterogeneous and multicultural patient population that utilized this facial phenotyping software at the point‐of‐care. We assessed the performance of F2G by comparing the suggested diagnoses to the patient's confirmed molecular diagnosis. Providers' overall experiences with the technology and trainees' educational experiences were assessed with questionnaires. We achieved an overall diagnostic yield of 57%. This increased to 82% when cases diagnosed with syndromes not recognized by F2G were removed. The mean rank of a confirmed diagnosis in the top 10 was 2.3 (CI 1.5–3.2) and the mean gestalt score 37.6%. The most commonly suggested diagnoses were Noonan syndrome, mucopolysaccharidosis, and 22q11.2 deletion syndrome. Our qualitative assessment revealed that clinicians and trainees saw value using the tool in practice. Overall, this work helped to implement an innovative patient care delivery model in clinical genetics that utilizes a facial phenotyping tool at the point‐of‐care. Our data suggest that F2G has utility in the genetics clinic as a clinical decision support tool in diverse populations, with a majority of patients having their eventual diagnosis listed in the top 10 suggested syndromes based on a photograph alone. It shows promise for further integration into clinical care and medical education, and we advocate for its continued use, adoption and refinement along with transparent and accountable industrial partnerships.
Significant improvements in automated image analysis have been achieved in recent years and tools are now increasingly being used in computer-assisted syndromology. However, the ability to recognize a syndromic facial gestalt might depend on the syndrome and may also be confounded by severity of phenotype, size of available training sets, ethnicity, age, and sex. Therefore, benchmarking and comparing the performance of deep-learned classification processes is inherently difficult. For a systematic analysis of these influencing factors we chose the lysosomal storage diseases mucolipidosis as well as mucopolysaccharidosis type I and II that are known for their wide and overlapping phenotypic spectra. For a dysmorphic comparison we used Smith-Lemli-Opitz syndrome as another inborn error of metabolism and Nicolaides-Baraitser syndrome as another disorder that is also characterized by coarse facies. A classifier that was trained on these five cohorts, comprising 289 patients in total, achieved a mean accuracy of 62%. We also developed a simulation framework to analyze the effect of potential confounders, such as cohort size, age, sex, or ethnic background on the distinguishability of phenotypes. We found that the true positive rate increases for all analyzed disorders for growing cohorts (n = [10...40]) while ethnicity and sex have no significant influence. The dynamics of the accuracies strongly suggest that the maximum distinguishability is a phenotype-specific value, which has not been reached yet for any of the studied disorders. This should also be a motivation to further intensify data sharing efforts, as computer-assisted syndrome classification can still be improved by enlarging the available training sets.
Electronic supplementary material
The online version of this article (10.1007/s10545-018-0174-3) contains supplementary material, which is available to authorized users.
Facial analysis systems are becoming available to healthcare providers to aid in the recognition of dysmorphic phenotypes associated with a multitude of genetic syndromes. These technologies automatically detect facial points and extract various measurements from images to recognise dysmorphic features and evaluate similarities to known facial patterns (gestalts). To evaluate such systems' usefulness for supporting the clinical practice of healthcare professionals, the recognition accuracy of the Cornelia de Lange Syndrome (CdLS) phenotype was examined with FDNA's automated Facial Dysmorphology Novel Analysis (FDNA) technology. In the first experiment, 2D facial images of CdLS patients with either an NIPBL or SMC1A gene mutation as well as non-CdLS patients which were assessed by dysmorphologists in a previous study were evaluated by the FDNA technology; the average detection rate of experts was 77% while the system's detection rate was 87%. In the second study, when a new set of NIPBL, SMC1A and non-CdLS patient photos was evaluated, the detection rate increased to 94%. The results from both studies indicated that the system's detection rate was comparable to that of dysmorphology experts. Therefore, utilising such technologies may be a useful tool in a clinical setting.
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.
Objectives:
To compare the detection of facial attributes by computer-based facial recognition software of 2-D images against standard, manual examination in fetal alcohol spectrum disorders (FASD).
Methods:
Participants were gathered from the Fetal Alcohol Syndrome Epidemiology Research database. Standard frontal and oblique photographs of children were obtained during a manual, in-person dysmorphology assessment. Images were submitted for facial analysis conducted by the facial dysmorphology novel analysis technology (an automated system), which assesses ratios of measurements between various facial landmarks to determine the presence of dysmorphic features. Manual blinded dysmorphology assessments were compared with those obtained via the computer-aided system.
Results:
Areas under the curve values for individual receiver-operating characteristic curves revealed the computer-aided system (0.88 ± 0.02) to be comparable to the manual method (0.86 ± 0.03) in detecting patients with FASD. Interestingly, cases of alcohol-related neurodevelopmental disorder (ARND) were identified more efficiently by the computer-aided system (0.84 ± 0.07) in comparison to the manual method (0.74 ± 0.04). A facial gestalt analysis of patients with ARND also identified more generalized facial findings compared to the cardinal facial features seen in more severe forms of FASD.
Conclusions:
We found there was an increased diagnostic accuracy for ARND via our computer-aided method. As this category has been historically difficult to diagnose, we believe our experiment demonstrates that facial dysmorphology novel analysis technology can potentially improve ARND diagnosis by introducing a standardized metric for recognizing FASD-associated facial anomalies. Earlier recognition of these patients will lead to earlier intervention with improved patient outcomes.
Authors – Hart TC, Hart PS
The objective of the study was to overview the role of genetic research in fostering translational studies of craniofacial diseases of dental interest. Background information is presented to illustrate influences affecting genetic research studies of Mendelian diseases. Genetic studies of amelogenesis imperfecta, dentinogenesis imperfecta, hereditary gingival fibromatosis and Papillon Lefèvre syndrome are reviewed. Findings are presented to illustrate how translational applications of clinical and basic research may improve clinical care. Clinical and basic science research has identified specific genes and mutations etiologically responsible for amelogenesis imperfecta, dentinogenesis imperfecta, hereditary gingival fibromatosis and Papillon Lefèvre syndrome. These findings are enabling researchers to understand how specific genetic alterations perturb normal growth and development of dental tissues. Identification of the genetic basis of these conditions is enabling clinicians and researchers to more fully understand the etiology and clinical consequences of these diseases of dental importance. Findings from genetic studies of dental diseases provide a basis for diagnostic genetic testing and development of therapeutic intervention strategies directed at the underlying disease etiology. These studies are advancing our understanding of the development of dental tissues in health and disease. The dental community must consider how to incorporate these developments into effective disease prevention paradigms to facilitate the diagnosis and treatment of individuals with genetic diseases.
Dysmorphology refers to study of human congenital malformations (birth defects). Most of the case reporting in dysmorphology is subjective and is based on experience of the reporting clinician. We have used the methods of geometric morphometrics to analyze the variation in faces of normal individuals and those with dysmorphic syndrome. We obtained photographs of 20 individuals with Rubinstein Taybi syndrome and 30 normal, age and sex matched individuals. The photographs were digitized with 16 landmarks on the face to obtain 32 "x" and "y" co-ordinates. These co-ordinates were then subjected to generalized procrustes superimposition in order to normalize for effects of size, rotation and position of image. The procrustes residuals thus obtained were then subjected to principal component analysis. The principal component analysis resulted in extraction of three important principal components explaining 41%, 17% and 14% of variance, respectively. Discriminant analysis could differentiate the two groups using first two principal component scores for each individual, with a predictive accuracy of 76% (Wilks lambda=0.725, chi2=15.09, d.f.=2, p=0.001). Binary logistic regression analysis showed predictive accuracy of 78% based on this model. The utility of the subjective evaluation of facial characteristics is multifold. The results of the analysis can be used as representatives of the facial dysmorphism for any genotype-phenotype association study. We conclude that application of the principles of geometric morphometrics to study of shape variation in facies of patients with dysmorphic syndromes appears to be a promising new area of research.