Antonio R. Porras

Down syndrome in diverse populations

Down syndrome is the most common cause of cognitive impairment and presents clinically with universally recognizable signs and symptoms. In this study, we focus on exam findings and digital facial analysis technology in individuals with Down syndrome in diverse populations. Photos and clinical information were collected on 65 individuals from 13 countries, 56.9% were male and the average age was 6.6 years (range 1 month to 26 years; SD = 6.6 years). Subjective findings showed that clinical features were different across ethnicities (Africans, Asians, and Latin Americans), including brachycephaly, ear anomalies, clinodactyly, sandal gap, and abundant neck skin, which were all significantly less frequent in Africans (P < 0.001, P < 0.001, P < 0.001, P < 0.05, and P < 0.05, respectively). Evaluation using a digital facial analysis technology of a larger diverse cohort of newborns to adults (n = 129 cases; n = 132 controls) was able to diagnose Down syndrome with a sensitivity of 0.961, specificity of 0.924, and accuracy of 0.943. Only the angles at medial canthus and ala of the nose were common significant findings amongst different ethnicities (Caucasians, Africans, and Asians) when compared to ethnically matched controls. The Asian group had the least number of significant digital facial biometrics at 4, compared to Caucasians at 8 and Africans at 7. In conclusion, this study displays the wide variety of findings across different geographic populations in Down syndrome and demonstrates the accuracy and promise of digital facial analysis technology in the diagnosis of Down syndrome internationally.

22q11.2 deletion syndrome in diverse populations.

22q11.2 deletion syndrome (22q11.2 DS) is the most common microdeletion syndrome and is underdiagnosed in diverse populations. This syndrome has a variable phenotype and affects multiple systems, making early recognition imperative. In this study, individuals from diverse populations with 22q11.2 DS were evaluated clinically and by facial analysis technology. Clinical information from 106 individuals and images from 101 were collected from individuals with 22q11.2 DS from 11 countries; average age was 11.7 and 47% were male. Individuals were grouped into categories of African descent (African), Asian, and Latin American. We found that the phenotype of 22q11.2 DS varied across population groups. Only two findings, congenital heart disease and learning problems, were found in greater than 50% of participants. When comparing the clinical features of 22q11.2 DS in each population, the proportion of individuals within each clinical category was statistically different except for learning problems and ear anomalies (P < 0.05). However, when Africans were removed from analysis, six additional clinical features were found to be independent of ethnicity (P ≥ 0.05). Using facial analysis technology, we compared 156 Caucasians, Africans, Asians, and Latin American individuals with 22q11.2 DS with 156 age and gender matched controls and found that sensitivity and specificity were greater than 96% for all populations. In summary, we present the varied findings from global populations with 22q11.2 DS and demonstrate how facial analysis technology can assist clinicians in making accurate 22q11.2 DS diagnoses. This work will assist in earlier detection and in increasing recognition of 22q11.2 DS throughout the world.

Noonan syndrome in diverse populations

Noonan syndrome (NS) is a common genetic syndrome associated with gain of function variants in genes in the Ras/MAPK pathway. The phenotype of NS has been well characterized in populations of European descent with less attention given to other groups. In this study, individuals from diverse populations with NS were evaluated clinically and by facial analysis technology. Clinical data and images from 125 individuals with NS were obtained from 20 countries with an average age of 8 years and female composition of 46%. Individuals were grouped into categories of African descent (African), Asian, Latin American, and additional/other. Across these different population groups, NS was phenotypically similar with only 2 of 21 clinical elements showing a statistically significant difference. The most common clinical characteristics found in all population groups included widely spaced eyes and low‐set ears in 80% or greater of participants, short stature in more than 70%, and pulmonary stenosis in roughly half of study individuals. Using facial analysis technology, we compared 161 Caucasian, African, Asian, and Latin American individuals with NS with 161 gender and age matched controls and found that sensitivity was equal to or greater than 94% for all groups, and specificity was equal to or greater than 90%. In summary, we present consistent clinical findings from global populations with NS and additionally demonstrate how facial analysis technology can support clinicians in making accurate NS diagnoses. This work will assist in earlier detection and in increasing recognition of NS throughout the world.

Identification of dysmorphic syndromes using landmark-specific local texture descriptors

The early detection of genetic disorders in infants is crucial for the timely management of patients and disease. The particular facial characteristics of patients affected by dysmorphic syndromes, which account to about half of genetic disorders, allows to identify positive cases prior to cytogenetic results, and avoid the overuse of genetic blood tests. However, the diagnostic accuracy by pediatricians is moderate. In this work, we present a general framework for the detection of genetic disorders from facial pictures, combining geometrical and texture features. Based on the 2D extension of Linear Discriminant Analysis, we propose the extraction of optimal landmark-specific Local Binary Pattern-based features. In particular, the proposed framework computes optimal local image filters and soft neighborhood weighting matrices that enhance the discriminative ability of the system. This new framework was tested on a database of 145 cases, including 73 pathological patients with 15 different genetic syndromes, obtaining a detection accuracy of 0.95.

Personalized Optimal Planning for the Surgical Correction of Metopic Craniosynostosis

We introduce a quantitative and automated method for personalized cranial shape remodeling via fronto-orbital advancement surgery. This paper builds on an objective method for automatic quantification of malformations caused by metopic craniosynostosis in children and presents a framework for personalized interventional planning. First, skull malformations are objectively quantified using a statistical atlas of normal cranial shapes. Then, we propose a method based on poly-rigid image registration that takes into account both the clinical protocol for fronto-orbital advancement and the physical constraints in the skull to plan the creation of the optimal post-surgical shape. Our automated surgical planning technique aims to minimize cranial malformations. The method was used to calculate the optimal shape for 11 infants with age 3.8±3.0 month old presenting metopic craniosynostosis and cranial malformations. The post-surgical cranial shape provided for each patient presented a significant average malformation reduction of 49% in the frontal cranial bones, and achieved shapes whose malformations were within healthy ranges. To our knowledge, this is the first work that presents an automatic framework for an objective and personalized surgical planning for craniosynostosis treatment.

Williams–Beuren syndrome in diverse populations

Williams–Beuren syndrome (WBS) is a common microdeletion syndrome characterized by a 1.5Mb deletion in 7q11.23. The phenotype of WBS has been well described in populations of European descent with not as much attention given to other ethnicities. In this study, individuals with WBS from diverse populations were assessed clinically and by facial analysis technology. Clinical data and images from 137 individuals with WBS were found in 19 countries with an average age of 11 years and female gender of 45%. The most common clinical phenotype elements were periorbital fullness and intellectual disability which were present in greater than 90% of our cohort. Additionally, 75% or greater of all individuals with WBS had malar flattening, long philtrum, wide mouth, and small jaw. Using facial analysis technology, we compared 286 Asian, African, Caucasian, and Latin American individuals with WBS with 286 gender and age matched controls and found that the accuracy to discriminate between WBS and controls was 0.90 when the entire cohort was evaluated concurrently. The test accuracy of the facial recognition technology increased significantly when the cohort was analyzed by specific ethnic population (P‐value < 0.001 for all comparisons), with accuracies for Caucasian, African, Asian, and Latin American groups of 0.92, 0.96, 0.92, and 0.93, respectively. In summary, we present consistent clinical findings from global populations with WBS and demonstrate how facial analysis technology can support clinicians in making accurate WBS diagnoses.

Construction of a Spatiotemporal Statistical Shape Model of Pediatric Liver from Cross-Sectional Data

This paper proposes a spatiotemporal statistical shape model of a pediatric liver, which has potential applications in computer-aided diagnosis of the abdomen. Shapes are analyzed in the space of a level set function, which has computational advantages over the diffeomorphic framework commonly employed in conventional studies. We first calculate the time-varying average of the mean shape development using a kernel regression technique with adaptive bandwidth. Then, eigenshape modes for every timepoint are calculated using principal component analysis with an additional regularization term that ensures the smoothness of the temporal change of the eigenshape modes. To further improve the performance, we applied data augmentation using a level set-based nonlinear morphing technique. The proposed algorithm was evaluated in the context of a spatiotemporal statistical shape modeling of a liver using 42 manually segmented livers from children whose age ranged from approximately 2 weeks to 95 months. Our method achieved a higher generalization and specificity ability compared with conventional methods.

Analysis of 3D Facial Dysmorphology in Genetic Syndromes from Unconstrained 2D Photographs

The quantification of facial dysmorphology is essential for the detection and diagnosis of genetic conditions. Facial analysis benefits from 3D image data, but 2D photography is more widely available at clinics. The aim of this paper is to analyze 3D facial dysmorphology using unconstrained (uncalibrated) 2D pictures at three orientations: frontal, left and right profiles. We estimate a unified 3D face shape by fitting a 3D morphable model (3DMM) to all the images by minimizing the differences between the 2D projected position of the selected 3D vertices in the 3DMM and their corresponding position in the 2D pictures. Using the estimated 3D face shape, we compute a set of facial dysmorphology measurements and train a classifier to identify genetic syndromes. Evaluated on a set of 48 subjects with and without genetic conditions, our method reduced the landmark detection errors obtained by using a single photograph by 44%, 48%, and 49% on the frontal photograph, left profile, and right profile, respectively. We achieved a point-to-point projection error of 1.98 ± 0.38% normalized to the size of face, significantly improving (p ≤ 0.01) the error obtained with state-of-the-art methods of 4.17 ± 2.83%. In addition, the geometric features calculated from the 3D reconstructed face obtained an accuracy of 73% in the detection of facial dysmorphology associated to genetic syndromes, compared with the error of 58% using state-of-the-art methods from 2D pictures. That accuracy increased to 96% when we included local texture information. Our results demonstrate the potential of this framework to assist in the earlier and remote detection of genetic syndromes throughout the world.

Radiation-free quantification of head malformations in craniosynostosis patients from 3D photography

The evaluation of cranial malformations plays an essential role both in the early diagnosis and in the decision to perform surgical treatment for craniosynostosis. In clinical practice, both cranial shape and suture fusion are evaluated using CT images, which involve the use of harmful radiation on children. Three-dimensional (3D) photography offers noninvasive, radiation-free, and anesthetic-free evaluation of craniofacial morphology. The aim of this study is to develop an automated framework to objectively quantify cranial malformations in patients with craniosynostosis from 3D photography. We propose a new method that automatically extracts the cranial shape by identifying a set of landmarks from a 3D photograph. Specifically, it registers the 3D photograph of a patient to a reference template in which the position of the landmarks is known. Then, the method finds the closest cranial shape to that of the patient from a normative statistical shape multi-atlas built from 3D photographs of healthy cases, and uses it to quantify objectively cranial malformations. We calculated the cranial malformations on 17 craniosynostosis patients and we compared them with the malformations of the normative population used to build the multi-atlas. The average malformations of the craniosynostosis cases were 2.68 ± 0.75 mm, which is significantly higher (p<0.001) than the average malformations of 1.70 ± 0.41 mm obtained from the normative cases. Our approach can support the quantitative assessment of surgical procedures for cranial vault reconstruction without exposing pediatric patients to harmful radiation.

Locally Affine Diffeomorphic Surface Registration for Planning of Metopic Craniosynostosis Surgery

The outcome of cranial vault reconstruction for the surgical treatment of craniosynostosis heavily depends on the surgeons expertise because of the lack of an objective target shape. We introduce a surface-based diffeomorphic registration framework to create the optimal post-surgical cranial shape during craniosynostosis treatment. Our framework estimates and labels where each bone piece needs to be cut using a reference template. Then, it calculates how much each bone piece needs to be translated and in which direction, using the closest normal shape from a multi-atlas as a reference. With our locally affine approach, the method also allows for bone bending, modeling independently the transformation of each bone piece while ensuring the consistency of the global transformation. We evaluated the optimal plan for 15 patients with metopic craniosynostosis. Our results showed that the automated surgical planning creates cranial shapes with a reduction in cranial malformations of 51.43% and curvature discrepancies of 35.09%, which are the two indices proposed in the literature to quantify cranial deformities objectively. In addition, the cranial shapes created were within healthy ranges.