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Timely medical care is crucial for optimal outcomes in children with cleft lip and palate. Undocumented migrant children face significant healthcare barriers. This study assesses the demographic characteristics and unmet surgical needs of undocumented children at our institution and analyzes state-level policies affecting their care., A retrospective review was conducted at a single institution between 2023 and 2024. Medicaid and Children's Health Insurance Program policies were reviewed as of August 2024, categorizing states by immigrant child healthcare coverage., Multidisciplinary cleft clinic in a pediatric referral center., Ten undocumented children were included, with clinical data and immigration status collected from social work and insurance records., Timing of initial cleft surgery, additional surgical needs, and surgical completion were assessed., Ten undocumented children were identified, with a mean age of 7 (range 1-10) years. Eight received primary cleft surgery in their home countries, but many had unmet surgical needs, including alveolar bone grafting (n = 4), oronasal fistula closure (n = 5), and dental care (n = 3). Twelve states provide comprehensive coverage regardless of immigration status, with 2 more expected by 2025. Twenty-three states cover only certain noncitizens without a waiting period, while 14 impose a 5-year delay. Broad coverage was concentrated in the Northeast and West ( P  = .002), with only one US-Mexico border state included., This study highlights barriers undocumented migrant children face in accessing cleft care, with disparities in state-funded policies potentially delaying critical interventions. Addressing these disparities is essential for equitable healthcare access., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from European Union’s Horizon 2020 for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. GD owns equity interest in Artrya Ltd and provides consultancy services. JK-C receives research funding from GE, Genetech and is a consultant at Siloam Vision. GPK advises some AI startups such as Gleamer.AI, FLUIDDA BV, NanoX Vision and was the founder of Quantib BV. SEP is a consultant for Circle Cardiovascular Imaging, Calgary, Alberta, Canada. BG is employed by Kheiron Medical Technologies and HeartFlow. PL has/had grants/sponsored research agreements from Radiomics SA, Convert Pharmaceuticals SA and LivingMed Biotech srl. He received a presenter fee and/or reimbursement of travel costs/consultancy fee (in cash or in kind) from Astra Zeneca, BHV srl, and Roche. PL has/had minority shares in the companies Radiomics SA, Convert pharmaceuticals SA, Comunicare SA, LivingMed Biotech srl, and Bactam srl. PL is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248 and PCT/NL2014/050728), licensed to Radiomics SA; one issued patent on mtDNA (PCT/EP2014/059089), licensed to ptTheragnostic/DNAmito; one granted patent on LSRT (PCT/ P126537PC00, US patent No 12 102 842), licensed to Varian; one issued patent on Radiomic signature of hypoxia (US patent No 11 972 867), licensed to a commercial entity; one issued patent on Prodrugs (WO2019EP64112) without royalties; one non-issued, non-licensed patents on Deep Learning-Radiomics (N2024889) and three non-patented inventions (softwares) licensed to ptTheragnostic/DNAmito, Radiomics SA and Health Innovation Ventures. ARP serves as advisor for mGeneRX in exchange for equity. JM receives royalties from GE, research grants from Siemens and is unpaid consultant for Nuance. HCW owns minority shares in the company Radiomics SA. JWG serves on several radiology society AI committees. LR advises an AI startup Neurlamind. CPL is a shareholder and advisor to Bunker Hill Health, GalileoCDS, Sirona Medical, Adra, and Kheiron Medical. He serves as a board member of Bunker Hill Health and a shareholder of whiterabbit.ai. He has served as a paid consultant to Sixth Street and Gilmartin Capital. His institution has received grants or gifts from Bunker Hill Health, Carestream, CARPL, Clairity, GE Healthcare, Google Cloud, IBM, Kheiron, Lambda, Lunit, Microsoft, Philips, Siemens Healthineers, Stability.ai, Subtle Medical, VinBrain, Visiana, Whiterabbit.ai, the Lowenstein Foundation, and the Gordon and Betty Moore Foundation. GSC is a statistics editor for the BMJ and a National Institute for Health and Care Research (NIHR) Senior Investigator. The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care. All other authors declare no competing interests.

Introduction: Pediatric craniofacial trauma, particularly from non-accidental trauma (NAT), is a significant cause of injury with enduring physical and psychological impacts. This study analyzes demographic patterns, injury characteristics, and trends in NAT-related craniofacial injuries to inform early identification, intervention, and prevention efforts., Methods: Analysis of the Healthcare Cost and Utilization Project Kids' Inpatient Database was performed for the years 2009 to 2019. Patients aged 0 to 17 with craniofacial injuries and a diagnosis of non-accidental trauma were identified based on current procedural codes; demographic and temporal trends were analyzed., Results: Overall, 306,115 patients with craniofacial injuries and 20,540 patients with non-accidental trauma (NAT) were identified. Patients with NAT represented 2.6% of all craniofacial injuries, with a greater proportion of males affected. On average, 39% of all NAT cases involved a craniofacial injury. The most common injury patterns were intracranial (50.1%), superficial head injuries (37.7%), and craniofacial fractures (31.9%. Isolated facial fractures were rare, comprising <1% of all cases. Although the average in-hospital mortality was 6.78%, temporal analysis demonstrated significant reductions in in-hospital mortality and length of hospitalization (P<0.001)., Conclusions: Pediatric craniofacial injuries from NAT remain a significant public health concern. However, the observed decrease in both hospitalization length and in-hospital deaths suggests improvements in early intervention and treatment outcomes. Continued efforts to refine diagnostic strategies, enhance provider awareness, and strengthen prevention initiatives are essential to mitigate the devastating impacts of NAT in the pediatric population., Competing Interests: The authors report no conflicts of interest., (Copyright © 2025 by Mutaz B. Habal, MD.)

3D photogrammetry is a cost-effective, non-invasive imaging modality that does not require the use of ionizing radiation or sedation. Therefore, it is specifically valuable in pediatrics and is used to support the diagnosis and longitudinal study of craniofacial developmental pathologies such as craniosynostosis - the premature fusion of one or more cranial sutures resulting in local cranial growth restrictions and cranial malformations. Analysis of 3D photogrammetry requires the identification of craniofacial landmarks to segment the head surface and compute metrics to quantify anomalies. Unfortunately, commercial 3D photogrammetry software requires intensive manual landmark placements, which is time-consuming and prone to errors. We designed and implemented SHAPE, a System for Head-shape Analysis and Pediatric Evaluation. It integrates our previously developed automated landmarking method in a visual computing pipeline to evaluate a patient's 3D photogram while allowing for manual confirmation and correction. It also automatically computes advanced metrics to quantify craniofacial anomalies and automatically creates a report that can be uploaded to the patient's electronic health record. We conducted a user study with a professional clinical photographer to compare SHAPE to the existing clinical workflow. We found that SHAPE allows for the evaluation of a craniofacial 3D photogram more than three times faster than the current clinical workflow (3.85 ± 0.99 vs. 13.07 ± 5.29 minutes, p < 0.001). Our qualitative study findings indicate that the SHAPE workflow is well aligned with the existing clinical workflow and that SHAPE has useful features and is easy to learn.

In this paper, we present the first publicly available 3D statistical facial shape model of babies, the Baby Face Model (BabyFM). Constructing a model of the facial geometry of babies entails specific challenges, such as occlusions, extreme and uncontrollable expressions, and data shortage. We address these challenges by proposing (1) a non-template dependent method that jointly estimates a 3D facial baby-specific template and the point-to-point correspondences; (2) a novel method to establish correspondences based on the spectral decomposition of the Laplace Beltrami Operator, which provides a more robust theoretical foundation than state-of-the-art methods; and (3) an asymmetry-swapping strategy to alleviate the shortage of large scale datasets by decoupling the identity-related and the asymmetry-related shape deformation fields. The latter leads to a data augmentation technique that we integrate within the Gaussian Process Morphable Model framework, providing a simple way of combining synthetic or sample covariance functions. We exhaustively evaluate each stage of our method and demonstrate that (1) when aiming at the 3D facial geometry of a baby, a specific model of babies is needed, since the pre-built publicly available models constructed with adults or older children are not able to accurately represent the facial shape of babies; (2) our spectral approach improves correspondences accuracy with respect to state-of-the-art-methods; and (3) the proposed data augmentation technique enhances the robustness of the BabyFM., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Antonia Alomar reports financial support was provided by Spain Ministry of Science and Innovation. Gemma Piella reports financial support was provided by Catalan Institution for Research and Advanced Studies. Marius George Linguraru reports financial support was provided by National Institute of Child Health and Human Development under the grant R42 HD08171203. Federico Sukno reports financial support was provided by Spain Ministry of Science and Innovation under the project grant PID2020-114083GB-I00. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

Purpose: The diagnosis of chronic increased intracranial pressure (IIP)is often based on subjective evaluation or clinical metrics with low predictive value. We aimed to quantify cranial bone changes associated with pediatric IIP using CT images and to identify patients at risk., Methods: We retrospectively quantified local cranial bone thickness and mineral density from the CT images of children with chronic IIP and compared their statistical differences to normative children without IIP adjusting for age, sex and image resolution. Subsequently, we developed a classifier to identify IIP based on these measurements. Finally, we demonstrated our methods to explore signs of IIP in patients with non-syndromic sagittal craniosynostosis (NSSC)., Results: We quantified a significant decrease of bone density in 48 patients with IIP compared to 1,018 normative subjects (P < .001), but no differences in bone thickness (P = .56 and P = .89 for age groups 0-2 and 2-10 years, respectively). Our classifier demonstrated 83.33% (95% CI: 69.24%, 92.03%) sensitivity and 87.13% (95% CI: 84.88%, 89.10%) specificity in identifying patients with IIP. Compared to normative subjects, 242 patients with NSSC presented significantly lower cranial bone density (P < .001), but no differences were found compared to patients with IIP (P = .57). Of patients with NSSC, 36.78% (95% CI: 30.76%, 43.22%) presented signs of IIP., Conclusion: Cranial bone changes associated with pediatric IIP can be quantified from CT images to support earlier diagnoses of IIP, and to study the presence of IIP secondary to cranial pathology such as non-syndromic sagittal craniosynostosis., (© 2024. The Author(s).)

Emotional experience is central to a fulfilling life. Although exposure to negative experiences is inevitable, an individual's emotion regulation response may buffer against psychopathology. Identification of neural activation patterns associated with emotion regulation via an fMRI task is a promising and non-invasive means of furthering our understanding of the how the brain engages with negative experiences. Prior work has applied multivariate pattern analysis to identify signatures of response to negative emotion-inducing images; we adapt these techniques to establish novel neural signatures associated with conscious efforts to modulate emotional response. We model voxel-level activation via LASSO principal components regression and linear discriminant analysis to predict if a subject was engaged in emotion regulation and to identify brain regions which define this emotion regulation signature. We train our models using 82 participants and evaluate them on a holdout sample of 40 participants, demonstrating an accuracy up to 82.5% across three classes. Our results suggest that emotion regulation produces a unique signature that is differentiable from passive viewing of negative and neutral imagery., (© 2024. The Author(s).)

Background Health care access disparities and lack of inclusion in clinical research have been well documented for marginalized populations. However, few studies exist examining the research funding of institutions that serve historically underserved groups. Purpose To assess the relationship between research funding awarded to radiology departments by the National Institutes of Health (NIH) and Lown Institute Hospitals Index rankings for inclusivity and community benefit. Materials and Methods This retrospective study included radiology departments awarded funding from the NIH between 2017 and 2021. The 2021 Lown Institute Hospitals Index rankings for inclusivity and community benefit were examined. The inclusivity metric measures how similar a hospital's patient population is to the surrounding community in terms of income, race and ethnicity, and education level. The community benefit metric measures charity care spending, Medicaid as a proportion of patient revenue, and other community benefit spending. Linear regression and Pearson correlation coefficients ( r values) were used to evaluate the relationship between aggregate NIH radiology department research funding and measures of inclusivity and community benefit. Results Seventy-five radiology departments that received NIH funding ranging from $195 000 to $216 879 079 were included. A negative correlation was observed between the amount of radiology department research funding received and institutional rankings for serving patients from racial and/or ethnic minorities ( r = -0.34; P < .001), patients with low income ( r = -0.44; P < .001), and patients with lower levels of education ( r = -0.46; P < .001). No correlation was observed between the amount of radiology department research funding and institutional rankings for charity care spending ( r = -0.19; P = .06), community investment ( r = -0.04; P = .68), and Medicaid as a proportion of patient revenue ( r = -0.10; P = .22). Conclusion Radiology departments that received more NIH research funding were less likely to serve patients from racial and/or ethnic minorities and patients who had low income or lower levels of education. © RSNA, 2024 See also the editorial by Mehta and Rosen in this issue.

We present the first data-driven pediatric model that explains cranial sutural growth in the pediatric population. We segmented the cranial bones in the neurocranium from the cross-sectional CT images of 2068 normative subjects (age 0-10 years), and we used a 2D manifold-based cranial representation to establish local anatomical correspondences between subjects guided by the location of the cranial sutures. We designed a diffeomorphic spatiotemporal model of cranial bone development as a function of local sutural growth rates, and we inferred its parameters statistically from our cross-sectional dataset. We used the constructed model to predict growth for 51 independent normative patients who had longitudinal images. Moreover, we used our model to simulate the phenotypes of single suture craniosynostosis, which we compared to the observations from 212 patients. We also evaluated the accuracy predicting personalized cranial growth for 10 patients with craniosynostosis who had pre-surgical longitudinal images. Unlike existing statistical and simulation methods, our model was inferred from real image observations, explains cranial bone expansion and displacement as a consequence of sutural growth and it can simulate craniosynostosis. This pediatric cranial suture growth model constitutes a necessary tool to study abnormal development in the presence of cranial suture pathology., (© 2023. The Author(s).)

Background and Objective: Accurate and repeatable detection of craniofacial landmarks is crucial for automated quantitative evaluation of head development anomalies. Since traditional imaging modalities are discouraged in pediatric patients, 3D photogrammetry has emerged as a popular and safe imaging alternative to evaluate craniofacial anomalies. However, traditional image analysis methods are not designed to operate on unstructured image data representations such as 3D photogrammetry., Methods: We present a fully automated pipeline to identify craniofacial landmarks in real time, and we use it to assess the head shape of patients with craniosynostosis using 3D photogrammetry. To detect craniofacial landmarks, we propose a novel geometric convolutional neural network based on Chebyshev polynomials to exploit the point connectivity information in 3D photogrammetry and quantify multi-resolution spatial features. We propose a landmark-specific trainable scheme that aggregates the multi-resolution geometric and texture features quantified at every vertex of a 3D photogram. Then, we embed a new probabilistic distance regressor module that leverages the integrated features at every point to predict landmark locations without assuming correspondences with specific vertices in the original 3D photogram. Finally, we use the detected landmarks to segment the calvaria from the 3D photograms of children with craniosynostosis, and we derive a new statistical index of head shape anomaly to quantify head shape improvements after surgical treatment., Results: We achieved an average error of 2.74 ± 2.70 mm identifying Bookstein Type I craniofacial landmarks, which is a significant improvement compared to other state-of-the-art methods. Our experiments also demonstrated a high robustness to spatial resolution variability in the 3D photograms. Finally, our head shape anomaly index quantified a significant reduction of head shape anomalies as a consequence of surgical treatment., Conclusion: Our fully automated framework provides real-time craniofacial landmark detection from 3D photogrammetry with state-of-the-art accuracy. In addition, our new head shape anomaly index can quantify significant head phenotype changes and can be used to quantitatively evaluate surgical treatment in patients with craniosynostosis., Competing Interests: Declaration of Competing Interest The authors have no competing financial interests to declare., (Copyright © 2023. Published by Elsevier B.V.)

Image segmentation, labeling, and landmark detection are essential tasks for pediatric craniofacial evaluation. Although deep neural networks have been recently adopted to segment cranial bones and locate cranial landmarks from computed tomography (CT) or magnetic resonance (MR) images, they may be hard to train and provide suboptimal results in some applications. First, they seldom leverage global contextual information that can improve object detection performance. Second, most methods rely on multi-stage algorithm designs that are inefficient and prone to error accumulation. Third, existing methods often target simple segmentation tasks and have shown low reliability in more challenging scenarios such as multiple cranial bone labeling in highly variable pediatric datasets. In this paper, we present a novel end-to-end neural network architecture based on DenseNet that incorporates context regularization to jointly label cranial bone plates and detect cranial base landmarks from CT images. Specifically, we designed a context-encoding module that encodes global context information as landmark displacement vector maps and uses it to guide feature learning for both bone labeling and landmark identification. We evaluated our model on a highly diverse pediatric CT image dataset of 274 normative subjects and 239 patients with craniosynostosis (age 0.63 ± 0.54 years, range 0-2 years). Our experiments demonstrate improved performance compared to state-of-the-art approaches.

Quantitative evaluation of pediatric craniofacial anomalies relies on the accurate identification of anatomical landmarks and structures. While segmentation and landmark detection methods in standard clinical images are available in the literature, image-based methods are not directly applicable to 3D photogrammetry because of its unstructured nature consisting in variable numbers of vertices and polygons. In this work, we propose a graph-based convolutional neural network based on Chebyshev polynomials that exploits vertex coordinates, polygonal connectivity, and surface normal vectors to extract multi-resolution spatial features from the 3D photographs. We then aggregate them using a novel weighting scheme that accounts for local spatial resolution variability in the data. We also propose a new trainable regression scheme based on the probabilistic distances between each original vertex and the anatomical landmarks to calculate coordinates from the aggregated spatial features. This approach allows calculating accurate landmark coordinates without assuming correspondences with specific vertices in the original mesh. Our method achieved state-of-the-art landmark detection errors.

Automatic anatomical segmentation and landmark localization in medical images are important tasks during craniofacial analysis. While deep neural networks have been recently applied to segment cranial bones and identify cranial landmarks from computed tomography (CT) or magnetic resonance (MR) images, existing methods often provide suboptimal and sometimes unrealistic results because they do not incorporate contextual image information. Additionally, most state-of-the-art deep learning methods for cranial bone segmentation and landmark detection rely on multi-stage data processing pipelines, which are inefficient and prone to errors. In this paper, we propose a novel context encoding-constrained neural network for single-stage cranial bone labeling and landmark localization. Specifically, we design and incorporate a novel context encoding module into a U-Net-like architecture. We explicitly enforce the network to capture context-related features for representation learning so pixel-wise predictions are not isolated from the image context. In addition, we introduce a new auxiliary task to model the relative spatial configuration of different anatomical landmarks, which serves as an additional regularization that further refines network predictions. The proposed method is end-to-end trainable for single-stage cranial bone labeling and landmark localization. The method was evaluated on a highly diverse pediatric 3D CT image dataset with 274 subjects. Our experiments demonstrate superior performance of our method compared to state-of-the-art approaches.

Available normative references of cranial bone development and suture fusion are incomplete or based on simplified assumptions due to the lack of large datasets. We present a fully data-driven normative model that represents the age- and sex-specific variability of bone shape, thickness, and density between birth and 10 years of age at every location of the calvaria., Methods: The model was built using a cross-sectional and multi-institutional pediatric computed tomography image dataset with 2068 subjects without cranial pathology (age 0-10 years). We combined principal component analysis and temporal regression to build a statistical model of cranial bone development at every location of the calvaria. We studied the influences of sex on cranial bone growth, and our bone density model allowed quantifying for the first time suture fusion as a continuous temporal process. We evaluated the predictive accuracy of our model using an independent longitudinal image dataset of 51 subjects., Results: Our model achieved temporal predictive errors of 2.98 ± 0.69 mm, 0.27 ± 0.29 mm, and 76.72 ± 91.50 HU in cranial bone shape, thickness, and mineral density changes, respectively. Significant sex differences were found in intracranial volume and bone surface areas ( P < 0.01). No significant differences were found in cephalic index, bone thickness, mineral density, or suture fusion., Conclusions: We presented the first pediatric age- and sex-specific statistical reference for local cranial bone shape, thickness, and mineral density changes. We showed its predictive accuracy using an independent longitudinal dataset, we studied developmental differences associated with sex, and we quantified suture fusion as a continuous process., Competing Interests: Disclosure: Jiawei Liu, Marius George Linguraru, and Antonio R. Porras are supported by the National Institute of Dental and Craniofacial Research (NIDCR) under grant number R00DE027993. Natasha Lepore, Marius George Linguraru, and Antonio R. Porras are also supported by NIDCR under grant number R01DE030286. Connor Elkhill is supported by the National Library of Medicine under grant number T15LM009451. The other authors have no financial interest to declare., (Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons.)

Background: The surgical correction of metopic craniosynostosis usually relies on the subjective judgment of surgeons to determine the configuration of the cranial bone fragments and the degree of overcorrection. This study evaluates the effectiveness of a new approach for automatic planning of fronto-orbital advancement based on statistical shape models and including overcorrection., Methods: This study presents a planning software to automatically estimate osteotomies in the fronto-orbital region and calculate the optimal configuration of the bone fragments required to achieve an optimal postoperative shape. The optimal cranial shape is obtained using a statistical head shape model built from 201 healthy subjects (age 23 ± 20 months; 89 girls). Automatic virtual plans were computed for nine patients (age 10.68 ± 1.73 months; four girls) with different degrees of overcorrection, and compared with manual plans designed by experienced surgeons., Results: Postoperative cranial shapes generated by automatic interventional plans present accurate matching with normative morphology and enable to reduce the malformations in the fronto-orbital region by 82.01 ± 6.07%. The system took on average 19.22 seconds to provide the automatic plan, and allows for personalized levels of overcorrection. The automatic plans with an overcorrection of 7 mm in minimal frontal breadth provided the closest match (no significant difference) to the manual plans., Conclusions: The automatic software technology effectively achieves correct cranial morphometrics and volumetrics with respect to normative cranial shapes. The automatic approach has the potential to reduce the duration of preoperative planning, reduce inter-surgeon variability, and provide consistent surgical outcomes., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons.)

Background: Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We, therefore, aimed to develop and evaluate a machine learning-based screening technology using facial photographs to evaluate a child's risk of presenting with a genetic syndrome for use at the point of care., Methods: In this retrospective study, we developed a facial deep phenotyping technology based on deep neural networks and facial statistical shape models to screen children for genetic syndromes. We trained the machine learning models on facial photographs from children (aged <21 years) with a clinical or molecular diagnosis of a genetic syndrome and controls without a genetic syndrome matched for age, sex, and race or ethnicity. Images were obtained from three publicly available databases (the Atlas of Human Malformations in Diverse Populations of the National Human Genome Research Institute, Face2Gene, and the dataset available from Ferry and colleagues) and the archives of the Children's National Hospital (Washington, DC, USA), in addition to photographs taken on a standard smartphone at the Children's National Hospital. We designed a deep learning architecture structured into three neural networks, which performed image standardisation (Network A), facial morphology detection (Network B), and genetic syndrome risk estimation, accounting for phenotypic variations due to age, sex, and race or ethnicity (Network C). Data were divided randomly into 40 groups for cross validation, and the performance of the model was evaluated in terms of accuracy, sensitivity, and specificity in both the total population and stratified by race or ethnicity, age, and sex., Findings: Our dataset included 2800 facial photographs of children (1318 [47%] female and 1482 [53%] male; 1576 [56%] White, 432 [15%] African, 430 [15%] Hispanic, and 362 [13%] Asian). 1400 children with 128 genetic conditions were included (the most prevalent being Williams-Beuren syndrome [19%], Cornelia de Lange syndrome [17%], Down syndrome [16%], 22q11.2 deletion [13%], and Noonan syndrome [12%] syndrome) in addition to 1400 photographs of matched controls. In the total population, our deep learning-based model had an accuracy of 88% (95% CI 87-89) for the detection of a genetic syndrome, with 90% sensitivity (95% CI 88-92) and 86% specificity (95% CI 84-88). Accuracy was greater in White (90%, 89-91) and Hispanic populations (91%, 88-94) than in African (84%, 81-87) and Asian populations (82%, 78-86). Accuracy was also similar in male (89%, 87-91) and female children (87%, 85-89), and similar in children younger than 2 years (86%, 84-88) and children aged 2 years or older (eg, 89% [87-91] for those aged 2 years to <5 years)., Interpretation: This genetic screening technology could support early risk stratification at the point of care in global populations, which has the potential accelerate diagnosis and reduce mortality and morbidity through preventive care., Funding: Children's National Hospital and Government of Abu Dhabi., Competing Interests: Declaration of interests This technology is protected by US patent numbers 9443132 (to MGL, MS, and KR), and 10204260 (to MGL and MS). The software is also protected by the copyright with assigned case and SR number 1–8317833951 (to ARP and MGL). All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Down syndrome is one of the most common chromosomal anomalies affecting the world's population, with an estimated frequency of 1 in 700 live births. Despite its relatively high prevalence, diagnostic rates based on clinical features have remained under 70% for most of the developed world and even lower in countries with limited resources. While genetic and cytogenetic confirmation greatly increases the diagnostic rate, such resources are often non-existent in many low- and middle-income countries, particularly in Sub-Saharan Africa. To address the needs of countries with limited resources, the implementation of mobile, user-friendly and affordable technologies that aid in diagnosis would greatly increase the odds of success for a child born with a genetic condition. Given that the Democratic Republic of the Congo is estimated to have one of the highest rates of birth defects in the world, our team sought to determine if smartphone-based facial analysis technology could accurately detect Down syndrome in individuals of Congolese descent. Prior to technology training, we confirmed the presence of trisomy 21 using low-cost genomic applications that do not need advanced expertise to utilize and are available in many low-resourced countries. Our software technology trained on 132 Congolese subjects had a significantly improved performance (91.67% accuracy, 95.45% sensitivity, 87.88% specificity) when compared to previous technology trained on individuals who are not of Congolese origin (p < 5%). In addition, we provide the list of most discriminative facial features of Down syndrome and their ranges in the Congolese population. Collectively, our technology provides low-cost and accurate diagnosis of Down syndrome in the local population., (Copyright © 2021 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Introduction: Patients with Noonan and Williams-Beuren syndrome present similar facial phenotypes modulated by their ethnic background. Although distinctive facial features have been reported, studies show a variable incidence of those characteristics in populations with diverse ancestry. Hence, a differential diagnosis based on reported facial features can be challenging. Although accurate diagnoses are possible with genetic testing, they are not available in developing and remote regions., Methods: We used a facial analysis technology to identify the most discriminative facial metrics between 286 patients with Noonan and 161 with Williams-Beuren syndrome with diverse ethnic background. We quantified the most discriminative metrics, and their ranges both globally and in different ethnic groups. We also created population-based appearance images that are useful not only as clinical references but also for training purposes. Finally, we trained both global and ethnic-specific machine learning models with previous metrics to distinguish between patients with Noonan and Williams-Beuren syndromes., Results: We obtained a classification accuracy of 85.68% in the global population evaluated using cross-validation, which improved to 90.38% when we adapted the facial metrics to the ethnicity of the patients (p = 0.024)., Conclusion: Our facial analysis provided for the first time quantitative reference facial metrics for the differential diagnosis Noonan and Williams-Beuren syndromes in diverse populations., (© 2021 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals LLC.)

Purpose: Surgical correction of metopic craniosynostosis typically involves open cranial vault remodeling. Accurate translation of the virtual surgical plan into the operating room is challenging due to the lack of tools for intraoperative analysis of the surgical outcome. This study aimed to evaluate the feasibility of using a hand-held 3D photography device for intraoperative evaluation and guidance during cranial vault surgical reconstruction., Methods: A hand-held structured light scanner was used for intraoperative 3D photography during five craniosynostosis surgeries, obtaining 3D models of skin and bone surfaces before and after the remodeling. The accuracy of this device for 3D modeling and morphology quantification was evaluated using preoperative computed tomography imaging as gold-standard. In addition, the time required for intraoperative 3D photograph acquisition was measured., Results: The average error of intraoperative 3D photography was 0.30 mm. Moreover, the interfrontal angle and the transverse forehead width were accurately measured in the 3D photographs with an average error of 0.72 degrees and 0.62 mm. Surgeon's feedback indicates that this technology can be integrated into the surgical workflow without substantially increasing surgical time., Conclusion: Hand-held 3D photography is an accurate technique for objective quantification of intraoperative cranial vault morphology and guidance during metopic craniosynostosis surgical reconstruction. This noninvasive technique does not substantially increase surgical time and does not require exposure to ionizing radiation, presenting a valuable alternative to computed tomography imaging. The proposed methodology can be integrated into the surgical workflow to assist during cranial vault remodeling and ensure optimal surgical outcomes.

The neurocranium changes rapidly in early childhood to accommodate the growing brain. Developmental disorders and environmental factors such as sleep position may lead to abnormal neurocranial maturation. Therefore, it is important to understand how this structure develops, in order to provide a baseline for early detection of anomalies. However, its anatomy has not yet been well studied in early childhood due to the lack of available imaging databases. In hospitals, CT is typically used to image the neurocranium when a pathology is suspected, but the presence of ionizing radiation makes it harder to construct databases of healthy subjects. In this study, instead, we use a dataset of MRI data from healthy normal children in the age range of 6 months to 36 months to study the development of the neurocranium. After extracting its outline from the MRI data, we used a conformal geometry-based analysis pipeline to detect local thickness growth throughout this age span. These changes will help us understand cranial bone development with respect to the brain, as well as detect abnormal variations, which will in turn inform better treatment strategies for implicated disorders.

Deep learning strategies have become ubiquitous optimization tools for medical image analysis. With the appropriate amount of data, these approaches outperform classic methodologies in a variety of image processing tasks. However, rare diseases and pediatric imaging often lack extensive data. Specially, MRI are uncommon because they require sedation in young children. Moreover, the lack of standardization in MRI protocols introduces a strong variability between different datasets. In this paper, we present a general deep learning architecture for MRI homogenization that also provides the segmentation map of an anatomical region of interest. Homogenization is achieved using an unsupervised architecture based on variational autoencoder with cycle generative adversarial networks, which learns a common space (i.e. a representation of the optimal imaging protocol) using an unpaired image-to-image translation network. The segmentation is simultaneously generated by a supervised learning strategy. We evaluated our method segmenting the challenging anterior visual pathway using three brain T1-weighted MRI datasets (variable protocols and vendors). Our method significantly outperformed a non-homogenized multi-protocol U-Net.

Background: Current methods to analyze three-dimensional photography do not quantify intracranial volume, an important metric of development. This study presents the first noninvasive, radiation-free, accurate, and reproducible method to quantify intracranial volume from three-dimensional photography., Methods: In this retrospective study, cranial bones and head skin were automatically segmented from computed tomographic images of 575 subjects without cranial abnormality (average age, 5 ± 5 years; range, 0 to 16 years). The intracranial volume and the head volume were measured at the cranial vault region, and their relation was modeled by polynomial regression, also accounting for age and sex. Then, the regression model was used to estimate the intracranial volume of 30 independent pediatric patients from their head volume measured using three-dimensional photography. Evaluation was performed by comparing the estimated intracranial volume with the true intracranial volume of these patients computed from paired computed tomographic images; two growth models were used to compensate for the time gap between computed tomographic and three-dimensional photography., Results: The regression model estimated the intracranial volume of the normative population from the head volume calculated from computed tomographic images with an average error of 3.81 ± 3.15 percent (p = 0.93) and a correlation (R) of 0.96. The authors obtained an average error of 4.07 ± 3.01 percent (p = 0.57) in estimating the intracranial volume of the patients from three-dimensional photography using the regression model., Conclusion: Three-dimensional photography with image analysis provides measurement of intracranial volume with clinically acceptable accuracy, thus offering a noninvasive, precise, and reproducible method to evaluate normal and abnormal brain development in young children., Clinical Question/level of Evidence: Diagnostic, V.

Introduction: Latent cranial suture fusions may present with mild or absent phenotypic changes that make the clinical diagnosis challenging. Recent reports describe patients with sagittal synostosis and a normal cranial index (CI), a condition termed normocephalic sagittal craniosynostosis (NSC). The goal of this study is to evaluate the shape and intracranial volume (ICV) in a cohort of NSC patients using quantitative cranial shape analysis (CSA)., Methods: We identified 19 patients (7.5 ± 2.28 years) between 2011 and 2016, who presented to our hospital with NSC. Cranial index and CSA were measured from the computed tomography image. Cranial shape analysis calculates the distances between the patient's cranial shape and its closest normal shape. Intracranial volume was measured and compared to an established age-matched normative database., Results: Cranial index revealed 15 (78.9%) patients within the mesocephalic range and 4 patients (21.1%) in the brachycephalic range. Detailed CSA identified 15 (78.9%) patients with subtle phenotypic changes along the scaphocephalic spectrum (ie, subtle anterior and posterior elongation with inter-parietal narrowing) and 1 patient (5.3%) with isolated overdevelopment on the posterior part of the right parietal bone. Three patients (15.8%) had a CSA close to normal. Mean ICV was 1410.5 ± 192.77cc; most patients (78.9%) fell within ±2 standard deviations., Conclusion: Quantitative CSA revealed that most of the patients with NSC had cranial shape abnormalities, consistent with a forme fruste scaphocephaly that could not be otherwise recognized by clinical observation or CI. Given these findings, we propose the term occult scaphocephaly to describe this condition. The associated incidence of intracranial hypertension is unknown.

Background: Evaluation of surgical treatment for craniosynostosis is typically based on subjective visual assessment or simple clinical metrics of cranial shape that are prone to interobserver variability. Three-dimensional photography provides cheap and noninvasive information to assess surgical outcomes, but there are no clinical tools to analyze it. The authors aim to objectively and automatically quantify head shape from three-dimensional photography., Methods: The authors present an automatic method to quantify intuitive metrics of local head shape from three-dimensional photography using a normative statistical head shape model built from 201 subjects. The authors use these metrics together with a machine learning classifier to distinguish between patients with (n = 266) and without (n = 201) craniosynostosis (aged 0 to 6 years). The authors also use their algorithms to quantify objectively local surgical head shape improvements on 18 patients with presurgical and postsurgical three-dimensional photographs., Results: The authors' methods detected craniosynostosis automatically with 94.74 percent sensitivity and 96.02 percent specificity. Within the data set of patients with craniosynostosis, the authors identified correctly the fused sutures with 99.51 percent sensitivity and 99.13 percent specificity. When the authors compared quantitatively the presurgical and postsurgical head shapes of patients with craniosynostosis, they obtained a significant reduction of head shape abnormalities (p < 0.05), in agreement with the treatment approach and the clinical observations., Conclusions: Quantitative head shape analysis and three-dimensional photography provide an accurate and objective tool to screen for head shape abnormalities at low cost and avoiding imaging with radiation and/or sedation. The authors' automatic quantitative framework allows for the evaluation of surgical outcomes and has the potential to detect relapses., Clinical Question/level of Evidence: Diagnostic, I.

Parkinson disease (PD) is characterized by the loss of dopaminergic (DAergic) neurons linked to environmental toxicants that cause oxidative stress (OS). The aim of this investigation was to establish the molecular response of human mesenchymal stroma cells (MSCs) depleted of glutathione (GSH) by the specific inhibitor L-buthionine-sulfoximine (BSO) to 6-hydroxydopamine (6-OHDA) and/or N-acetylcysteine (NAC) co-treatment. We found that treatment with BSO (10 mM) plus 6-OHDA (200 μM) induced apoptosis in MSCs through an oxidative stress (OS) mechanism involving H 2 O 2 , reflected by the detection of dichlorofluorescein-positive (DCF+) cells and oxidation of DJ-1 Cys106-SH into DJ-1 Cys106-SO 3 ; an almost complete reduction in glutathione peroxidase 1 (GPX1) expression; activation of the transcription factor c-JUN, the pro-apoptotic protein BAX and BH-3-only protein PUMA; loss of mitochondrial membrane potential (∆Ψ m ); activation of the protease caspase-3 (CASP3) and apoptosis-inducing factor (AIF); chromatin condensation; and DNA fragmentation. Strikingly, co-treatment of MSCs with NAC (5 mM) and BSO + 6-OHDA significantly reduced the expression of OS and cell death markers but were unable to restore the expression of GPX1 compared to the expression in untreated or treated cells with NAC only. These findings highlighted the importance of the maintenance of the GSH-dependent (e.g., GPX1, GSH synthesis) and -independent (e.g., ROS scavenger molecules and thiol reducing activity) antioxidant systems (e.g., NAC) in the protection of MSCs from detrimental stress stimuli, thereby increasing the survival of stromal cells.

Cornelia de Lange syndrome (CdLS) is a dominant multisystemic malformation syndrome due to mutations in five genes-NIPBL, SMC1A, HDAC8, SMC3, and RAD21. The characteristic facial dysmorphisms include microcephaly, arched eyebrows, synophrys, short nose with depressed bridge and anteverted nares, long philtrum, thin lips, micrognathia, and hypertrichosis. Most affected individuals have intellectual disability, growth deficiency, and upper limb anomalies. This study looked at individuals from diverse populations with both clinical and molecularly confirmed diagnoses of CdLS by facial analysis technology. Clinical data and images from 246 individuals with CdLS were obtained from 15 countries. This cohort included 49% female patients and ages ranged from infancy to 37 years. Individuals were grouped into ancestry categories of African descent, Asian, Latin American, Middle Eastern, and Caucasian. Across these populations, 14 features showed a statistically significant difference. The most common facial features found in all ancestry groups included synophrys, short nose with anteverted nares, and a long philtrum with thin vermillion of the upper lip. Using facial analysis technology we compared 246 individuals with CdLS to 246 gender/age matched controls and found that sensitivity was equal or greater than 95% for all groups. Specificity was equal or greater than 91%. In conclusion, we present consistent clinical findings from global populations with CdLS while demonstrating how facial analysis technology can be a tool to support accurate diagnoses in the clinical setting. This work, along with prior studies in this arena, will assist in earlier detection, recognition, and treatment of CdLS worldwide., (© 2019 Wiley Periodicals, Inc.)

The evaluation of head malformations plays an essential role in the early diagnosis, the decision to perform surgery and the assessment of the surgical outcome of patients with craniosynostosis. Clinicians rely on two metrics to evaluate the head shape: head circumference (HC) and cephalic index (CI). However, they present a high inter-observer variability and they do not take into account the location of the head abnormalities. In this study, we present an automated framework to objectively quantify the head malformations, HC, and CI from three-dimensional (3D) photography, a radiation-free, fast and non-invasive imaging modality. Our method automatically extracts the head shape using a set of landmarks identified by registering the head surface of a patient to a reference template in which the position of the landmarks is known. Then, we quantify head malformations as the local distances between the patient's head and its closest normal from a normative statistical head shape multi-atlas. We calculated cranial malformations, HC, and CI for 28 patients with craniosynostosis, and we compared them with those computed from the normative population. Malformation differences between the two populations were statistically significant (p<0.05) at the head regions with abnormal development due to suture fusion. We also trained a support vector machine classifier using the malformations calculated and we obtained an improved accuracy of 91.03% in the detection of craniosynostosis, compared to 78.21% obtained with HC or CI. This method has the potential to assist in the longitudinal evaluation of cranial malformations after surgical treatment of craniosynostosis.

Metopic craniosynostosis is a condition caused by the premature fusion of the metopic cranial suture. If untreated, it can result into brain growth restriction, increased intra-cranial pressure, visual impairment, and cognitive delay. Fronto-orbital advancement is the widely accepted surgical approach to correct cranial shape abnormalities in patients with metopic craniosynostosis, but the outcome of the surgery remains very dependent on the expertise of the surgeon because of the lack of objective and personalized cranial shape metrics to target during the intervention. We propose in this paper a locally affine diffeomorphic surface registration framework to create an optimal interventional plan personalized to each patient. Our method calculates the optimal surgical plan by minimizing cranial shape abnormalities, which are quantified using objective metrics based on a normative model of cranial shapes built from 198 healthy cases. It is guided by clinical osteotomy templates for fronto-orbital advancement, and it automatically calculates how much and in which direction each bone piece needs to be translated, rotated, and/or bent. Our locally affine framework models separately the transformation of each bone piece while ensuring the consistency of the global transformation. We used our method to calculate the optimal surgical plan for 23 patients, obtaining a significant reduction of malformations (p < 0.001) between 40.38% and 50.85% in the simulated outcome of the surgery using different osteotomy templates. In addition, malformation values were within healthy ranges (p > 0.01).

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., (© 2018 Wiley Periodicals, Inc.)

Purpose: We sought to define features that describe the dynamic information in diuresis renograms for the early detection of clinically significant hydronephrosis caused by ureteropelvic junction obstruction., Materials and Methods: We studied the diuresis renogram of 55 patients with a mean ± SD age of 75 ± 66 days who had congenital hydronephrosis at initial presentation. Five patients had bilaterally affected kidneys for a total of 60 diuresis renograms. Surgery was performed on 35 kidneys. We extracted 45 features based on curve shape and wavelet analysis from the drainage curves recorded after furosemide administration. The optimal features were selected as the combination that maximized the ROC AUC obtained from a linear support vector machine classifier trained to classify patients as with or without obstruction. Using these optimal features we performed leave 1 out cross validation to estimate the accuracy, sensitivity and specificity of our framework. Results were compared to those obtained using post-diuresis drainage half-time and the percent of clearance after 30 minutes., Results: Our framework had 93% accuracy, including 91% sensitivity and 96% specificity, to predict surgical cases. This was a significant improvement over the same accuracy of 82%, including 71% sensitivity and 96% specificity obtained from half-time and 30-minute clearance using the optimal thresholds of 24.57 minutes and 55.77%, respectively., Conclusions: Our machine learning framework significantly improved the diagnostic accuracy of clinically significant hydronephrosis compared to half-time and 30-minute clearance. This aids in the clinical decision making process by offering a tool for earlier detection of severe cases and it has the potential to reduce the number of diuresis renograms required for diagnosis., (Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

There has been a recent emphasis in surgical science on supplementing surgical training outside of the Operating Room (OR). Combining simulation training with the current surgical apprenticeship enhances surgical skills in the OR, without increasing the time spent in the OR practicing. Computer-assisted surgical (CAS) planning consists of performing operative techniques virtually using three-dimensional (3D) computer-based models reconstructed from 3D cross-sectional imaging. The purpose of this paper is to present a CAS system to rehearse, visualize and quantify osteotomies, and demonstrate its usefulness in two different osteotomy surgical procedures, cranial vault reconstruction and femoral osteotomy. We found that the system could sufficiently simulate these two procedures. Our system takes advantage of the high-quality visualizations possible with 3DSlicer, as well as implements new infrastructure to allow for direct 3D interaction (cutting and positioning) with the bone models. We see the proposed osteotomy planner tool evolving towards incorporating different cutting templates to help depict several surgical scenarios, help 'trained' surgeons maintain operating skills, help rehearse a surgical sequence before heading to the OR, or even to help surgical planning for specific patient cases.

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 non-invasive, 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.

Background Aims: Stem cell transplantation is an excellent option for regenerative or replacement therapy. However, deleterious microenvironmental and endogenous factors (e.g., oxidative stress) compromise ongoing graft survival and longevity. Therefore, (transient or stable) genetically modified cells may be reasonably thought to resist oxidative stress-induced damage. Genetic engineering of mesenchymal stromal cells (MSCs) obtained from Wharton's jelly tissue may offer some therapeutic potential. PARKIN is a multifunctional ubiquitin ligase able to protect dopaminergic cells against stress-related signaling. We, therefore, evaluated the effect of the neurotoxicant 6-hydroxydopamine (6-OHDA) on regulated cell death signaling in MSCs and investigated whether overexpression of PARKIN in MSCs was capable of modulating the effect of 6-OHDA., Methods: We transiently transfected Wharton's jelly-derived MSCs with an mCherry-PARKIN vector using the Lipofectamine LTX method. Naïve MSCs and MSCs overexpressing PARKIN were exposed to increasing concentrations of 6-OHDA. We used light and fluorescence microscopy, flow cytometry, immunocytochemistry staining, in-cell Western and Western blot analysis., Results: After 12-24 h of 6-OHDA exposure, we detected dichlorofluorescein (DCF)-positive cells (80%) indicative of reactive oxygen species (H2O2) production, reduced cell viability (40-50%), decreased mitochondrial membrane potential (ΔΨm, ~35-45%), DNA fragmentation (18-30%), and G1-arrested cell cycle in the MSCs. 6-OHDA exposure increased the expression of the transcription factor c-JUN, increased the expression of the mitochondria maintenance Phosphatase and tensin homologue-induced putative kinase 1 (PINK1) protein and increased the expression of pro-apoptotic PUMA, caspase-3 and apoptosis-inducing factor (AIF). 6-OHDA exposure also significantly augmented the oxidation of the oxidative stress sensor, DJ-1. Overexpression of PARKIN in MSCs not only significantly reduced the expression of cell death and oxidative stress markers but also significantly reduced DCF-positive cells (~50% reduction)., Discussion: 6-OHDA induced apoptosis in MSCs via generation of H2O2, activation of c-JUN and PUMA, mitochondrial depolarization and nuclei fragmentation. Our findings suggest that PARKIN protects MSCs against 6-OHDA toxicity by partly interacting with H2O2, reducing the expression of c-JUN, PUMA, AIF and caspase-3, and maintaining the mitochondrial ΔΨm., (Copyright © 2017 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.)

Craniosynostosis is a congenital malformation of the infant skull typically treated via corrective surgery. To accurately quantify the extent of deformation and identify the optimal correction strategy, the patient-specific skull model extracted from a pre-surgical computed tomography (CT) image needs to be registered to an atlas of head CT images representative of normal subjects. Here, the authors present a robust multi-stage, multi-resolution registration pipeline to map a patient-specific CT image to the atlas space of normal CT images. The proposed registration pipeline first performs an initial optimisation at very low resolution to yield a good initial alignment that is subsequently refined at high resolution. They demonstrate the robustness of the proposed method by evaluating its performance on 560 head CT images of 320 normal subjects and 240 craniosynostosis patients and show a success rate of 92.8 and 94.2%, respectively. Their method achieved a mean surface-to-surface distance between the patient and template skull of <2.5 mm in the targeted skull region across both the normal subjects and patients.

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., (© 2017 Wiley Periodicals, Inc.)

The outcome of cranial vault reconstruction for the surgical treatment of craniosynostosis heavily depends on the surgeon's 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.

Background: The human mesenchymal stem cells derived from Wharton's jelly tissue (hWJ-MSCs) represent a tool for cell-based therapies and regenerative medicine. hWJ-MSCs form neurospheres (NSs) within 3-7 days. No data is available to establish the neuro-phenotypic markers and time of formation of nerve-like (NLCs) and glial cells from NSs derived from hWJ-MSCs. NEW METHOD: hWJ-MSCs were incubated with Fast-N-Spheres medium for 24 and 72h. The new formed NSs were in turn incubated with forskolin in neurogenic NeuroForsk medium for 1-7days., Results: hWJ-MSCs cultured with Fast-N-Spheres medium trans-differentiated into NSs in just 24h compared to 72h for hWJ-MSCs cultured with classic growth factor medium. The NSs generated from the Fast-N-Spheres medium expressed reduced levels SOX2, OCT4 and NANOG, as markers of pluripotency compared to undifferentiated hWJ-MSCs. The formed NSs exposed to NeuroForsk medium differentiated into NLCs in 4days as evidenced by high levels of protein expression of the neuronal markers, and no expression of the glial marker GFAP., Comparison With Existing Method(s): Currently, the formation and harvest of NSs is expensive and time consuming. Published protocols require 3-7days to form NSs from whole human umbilical cord MSCs. We report for the first time, to our knowledge, the differentiation of NSs-derived from hWJ-MSCs into NLCs., Conclusions: The fastest method to obtain NSs and NLCs from hWJ-MSCs takes only five days using the two-step incubation media Fast-N-Spheres and NeuroForsk., (Copyright © 2017 Elsevier B.V. All rights reserved.)

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., (© 2017 Wiley Periodicals, Inc.)

3D photography offers non-invasive, radiation-free, and anesthetic-free evaluation of craniofacial morphology. However, intracranial volume (ICV) quantification is not possible with current non-invasive imaging systems in order to evaluate brain development in children with cranial pathology. The aim of this study is to develop an automated, radiation-free framework to estimate ICV. Pairs of computed tomography (CT) images and 3D photographs were aligned using registration. We used the real ICV calculated from the CTs and the head volumes from their corresponding 3D photographs to create a regression model. Then, a template 3D photograph was selected as a reference from the data, and a set of landmarks defining the cranial vault were detected automatically on that template. Given the 3D photograph of a new patient, it was registered to the template to estimate the cranial vault area. After obtaining the head volume, the regression model was then used to estimate the ICV. Experiments showed that our volume regression model predicted ICV from head volumes with an average error of 5.81 ± 3.07% and a correlation (R 2 ) of 0.96. We also demonstrated that our automated framework quantified ICV from 3D photography with an average error of 7.02 ± 7.76%, a correlation (R 2 ) of 0.94, and an average estimation error for the position of the cranial base landmarks of 11.39 ± 4.3mm.

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. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Nasulysin-1, a new zinc-metalloproteinase from the snake venom of the hognose pit viper Porthidium nasutum, was purified to homogeneity using molecular exclusion chromatography and high performance liquid chromatography on a reverse phase column. The molecular mass of the purified enzyme was 25,900 kDa and pI 4.1, as determined by 1D and 2D polyacrylamide gel electrophoresis. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) analysis of the N-terminal amino acid sequence (1FSPRYIELVVVADHGMFKKYNSNLNTIR28; 1TASLANLEVWSK12; 1DLLPR6) of the purified nasulysin-1, shows close structural homology with other snake venom metalloproteinases isolated from different snake venoms. The purified nasulysin-1 showed specific apoptosis-inducing activity in Jurkat and K562 cells, a T-cell acute lymphocytic leukemia (ALL) and chronic myeloid leukemia (AML) cell model, respectively, without affecting the viability of human lymphocyte cells. After 48 h treatment, nasulysin-1 (20 μg/mL) induced loss of the mitochondrial membrane potential (ΔΨm), activated the apoptosis-inducing factor (AIF), activated the protease caspase-3, and induced chromatin condensation and DNA fragmentation, all hallmarks of apoptosis. These results strongly suggest that nasulysin-1 selectively induces apoptosis to eliminate leukemia cells. Thus, these data warrant further investigation into the use of the metalloproteinase protein, nasulysin-1 as a potential therapeutic agent for treating leukemia., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Although modern ultrasound acquisition systems allow recording of 3D echocardiographic images, tracking anatomical structures from them is still challenging. In addition, since these images are typically created from information obtained across several cardiac cycles, it is not yet possible to acquire high-quality 3D images from patients presenting varying heart rhythms. In this paper, we propose a method to estimate the motion field from multi-plane echocardiographic images of the left ventricle, which are acquired simultaneously during a single cardiac cycle. The method integrates tri-plane B-mode and tissue Doppler images acquired at different rotation angles around the long axis of the left ventricle. It uses a diffeomorphic continuous spatio-temporal transformation model with a spherical data representation for a better interpolation in the circumferential direction. This framework allows exploiting the spatial relation among the acquired planes. In addition, higher temporal resolution of the transformation in the beam direction is achieved by uncoupling the estimation of the different components of the velocity field. The method was validated using a realistic synthetic dataset including healthy and ischemic cases, obtaining errors of 0.14 ± 0.09 mm for displacements, 0.96 ± 1.03% for longitudinal strain and 3.94 ± 4.38% for radial strain estimation. In addition, the method was also demonstrated on a healthy volunteer and two patients with ischemia.

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.

We propose a technique for myocardial motion estimation based on image registration using both B-mode echocardiographic images and tissue Doppler sequences acquired interleaved. The velocity field is modeled continuously using B-splines and the spatiotemporal transform is constrained to be diffeomorphic. Images before scan conversion are used to improve the accuracy of the estimation. The similarity measure includes a model of the speckle pattern distribution of B-mode images. It also penalizes the disagreement between tissue Doppler velocities and the estimated velocity field. Registration accuracy is evaluated and compared to other alternatives using a realistic synthetic dataset, obtaining mean displacement errors of about 1 mm. Finally, the method is demonstrated on data acquired from six volunteers, both at rest and during exercise. Robustness is tested against low image quality and fast heart rates during exercise. Results show that our method provides a robust motion estimate in these situations.