Next-generation phenotyping in cat-eye syndrome based on computer-aided facial dysmorphology analysis of normal photographs.
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ABSTRACT:
SUBMITTER: Liehr T
PROVIDER: S-EPMC8580072 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
Next-generation phenotyping in cat-eye syndrome based on computer-aided facial dysmorphology analysis of normal photographs.
Liehr Thomas T Fleischer Nicole N Al-Rikabi Ahmed A
Molecular genetics & genomic medicine 20210825 10
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Project description:Facial asymmetries exist in all individuals. Due to these facial asymmetries that exist, a standardized approach in locating the occlusal plane that is parallel to the ala-tragus and interpupillary lines, may result in less than ideal esthetics in the final restoration. The challenge for the prosthodontist is to determine an acceptable occlusal plane with an individualized approach that can be used as a guide for alignment of the maxillary anterior teeth in cases that require their replacement or extensive restoration. The present study uses an inexpensive and standardized digital photographic technique along with computer assisted analysis to measure the asymmetries of the human face. Statistical Analysis used-Karl pearson's correlation coeffient was used. The correlation coefficient was then subjected to 't' test and 'p' value was used to find out the level of statistical significance. Left side of the face was found to be at a higher level than the right side.
| S-EPMC3332314 | biostudies-literature
Project description:BackgroundWhile 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.ObjectiveWe aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt.MethodsUsing 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.ResultsDeepGestalt 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.ConclusionsIf 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.
| S-EPMC10973953 | biostudies-literature
Project description:BackgroundCollectively, 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.ObjectiveThe 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.MethodsFrontal 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.ResultsWe 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).ConclusionsDeepGestalt 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.
| S-EPMC7644377 | biostudies-literature
Project description:In this contribution, a software system for computer-aided position planning of miniplates to treat facial bone defects is proposed. The intra-operatively used bone plates have to be passively adapted on the underlying bone contours for adequate bone fragment stabilization. However, this procedure can lead to frequent intra-operatively performed material readjustments especially in complex surgical cases. Our approach is able to fit a selection of common implant models on the surgeon's desired position in a 3D computer model. This happens with respect to the surrounding anatomical structures, always including the possibility of adjusting both the direction and the position of the used osteosynthesis material. By using the proposed software, surgeons are able to pre-plan the out coming implant in its form and morphology with the aid of a computer-visualized model within a few minutes. Further, the resulting model can be stored in STL file format, the commonly used format for 3D printing. Using this technology, surgeons are able to print the virtual generated implant, or create an individually designed bending tool. This method leads to adapted osteosynthesis materials according to the surrounding anatomy and requires further a minimum amount of money and time.
| S-EPMC5560576 | biostudies-other
Project description:Facial gender surgery (FGS) involves major surgical modification of the craniofacial soft tissues and skeleton. Computer-aided surgery (CAS) has improved precision and accuracy of osteotomies and decreased operative time in complex reconstructive craniofacial surgery. FGS is a natural application for CAS because the procedures are not only technically challenging but also demand a high standard of aesthetic results. Planning FGS cases virtually enables better and more reproducible results through simulated surgical planning and precise execution of osteotomies in surgical fields with limited exposure. We describe our experience with CAS in FGS for each of the facial thirds to introduce new concepts for conceptual planning of osteotomy design and patient-specific implants.
| S-EPMC9187168 | biostudies-literature
Project description:An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.
| S-EPMC8115570 | biostudies-literature
Project description:PurposeThe interpretation of genetic variants after genome-wide analysis is complex in heterogeneous disorders such as intellectual disability (ID). We investigate whether algorithms can be used to detect if a facial gestalt is present for three novel ID syndromes and if these techniques can help interpret variants of uncertain significance.MethodsFacial features were extracted from photos of ID patients harboring a pathogenic variant in three novel ID genes (PACS1, PPM1D, and PHIP) using algorithms that model human facial dysmorphism, and facial recognition. The resulting features were combined into a hybrid model to compare the three cohorts against a background ID population.ResultsWe validated our model using images from 71 individuals with Koolen-de Vries syndrome, and then show that facial gestalts are present for individuals with a pathogenic variant in PACS1 (p = 8 × 10-4), PPM1D (p = 4.65 × 10-2), and PHIP (p = 6.3 × 10-3). Moreover, two individuals with a de novo missense variant of uncertain significance in PHIP have significant similarity to the expected facial phenotype of PHIP patients (p < 1.52 × 10-2).ConclusionOur results show that analysis of facial photos can be used to detect previously unknown facial gestalts for novel ID syndromes, which will facilitate both clinical and molecular diagnosis of rare and novel syndromes.
| S-EPMC6752476 | biostudies-literature
Project description:Multimaterials deposition, a distinct advantage in bioprinting, overcomes material's limitation in hydrogel-based bioprinting. Multimaterials are deposited in a build/support configuration to improve the structural integrity of three-dimensional bioprinted construct. A combination of rapid cross-linking hydrogel has been chosen for the build/support setup. The bioprinted construct was further chemically cross-linked to ensure a stable construct after print. This paper also proposes a file segmentation and preparation technique to be used in bioprinting for printing freeform structures.
| S-EPMC7294690 | biostudies-literature
Project description:BackgroundAutism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset; 3) to evaluate the performance of classifiers obtained by a new classification algorithm (Random Forest) using the previous and the current datasets.MethodsThe previously approach was replicated with a new stimulus and new sample, 44 from the Typical Development group and 33 from the ASD group. After the replication, Random Forest classifier was tested to substitute Neural Networks algorithm.ResultsThe test with the trained classifier reached an AUC of 0.56, suggesting that the trained classifier requires retraining of the VAMs when changing the stimulus. The replication results reached an AUC of 0.71, indicating the potential of generalization of the approach for aiding ASD diagnosis, as long as the stimulus is similar to the originally proposed. The results achieved with Random Forest were superior to those achieved with the original approach, with an average AUC of 0.95 for the previous dataset and 0.74 for the new dataset.ConclusionIn summary, the results of the replication experiment were satisfactory, which suggests the robustness of the approach and the VAM-based approaches feasibility to aid in ASD diagnosis. The proposed method change improved the classification performance. Some limitations are discussed and additional studies are encouraged to test other conditions and scenarios.
| S-EPMC10722824 | biostudies-literature
Project description:A full-term female baby, a product of non-consanguineous marriage, was born at 37 weeks of gestation with a birth weight of 2.08 kg. Antenatal scan at 31 weeks revealed complex congenital heart disease with a hypoplastic right ventricle, pulmonary atresia and an intact septum. Immediately after birth, the infant was shifted to the nursery and was started on intravenous fluids and infusion prostaglandin E1 (Alprostidil). On examination, she had microcephaly, periorbital puffiness, a long philtrum, a broad nasal bridge and retrognathia, up slanting palpebral fissures, widely spaced nipples, a sacral dimple and right upper limb postaxial polydactyly. Postnatal echocardiography confirmed a large ostium secundum atrial septal defect with left to right shunt, right ventricle hypoplasia, pulmonary atresia with an intact septum and a large vertical patent ductus arteriosus. Ophthalmological examination showed a bilateral chorioretinal coloboma sparing disc and fovea. Karyotyping showed an extra small marker chromosome suggestive of the Cat eye syndrome.
| S-EPMC4039909 | biostudies-literature