Hasil untuk "Pediatrics"

Chloe Scholten, Elyssa M. McMaster, Adam M. Saunders et al.

In cases of prevalent diseases and disorders, such as Prenatal Alcohol Exposure (PAE), multi-site data collection allows for increased study samples. However, multi-site studies introduce additional variability through heterogeneous collection materials, such as scanner and acquisition protocols, which confound with biologically relevant signals. Neuroscientists often utilize statistical methods on image-derived metrics, such as volume of regions of interest, after all image processing to minimize site-related variance. HACA3, a deep learning harmonization method, offers an opportunity to harmonize image signals prior to metric quantification; however, HACA3 has not yet been validated in a pediatric cohort. In this work, we investigate HACA3's ability to remove site-related variance and preserve biologically relevant signal compared to a statistical method, neuroCombat, and pair HACA3 processing with neuroCombat to evaluate the efficacy of multiple harmonization methods in a pediatric (age 7 to 21) population across three unique scanners with controls and cases of PAE with downstream MaCRUISE volume metrics. We find that HACA3 qualitatively improves inter-site contrast variations, but statistical methods reduce greater site-related variance within the MaCRUISE volume metrics following an ANCOVA test, and HACA3 relies on follow-up statistical methods to approach maximal biological preservation in this context.

Liangyang Ouyang, Yuki Sakai, Ryosuke Furuta et al.

This paper addresses the task of assessing PICU team's leadership skills by developing an automated analysis framework based on egocentric vision. We identify key behavioral cues, including fixation object, eye contact, and conversation patterns, as essential indicators of leadership assessment. In order to capture these multimodal signals, we employ Aria Glasses to record egocentric video, audio, gaze, and head movement data. We collect one-hour videos of four simulated sessions involving doctors with different roles and levels. To automate data processing, we propose a method leveraging REMoDNaV, SAM, YOLO, and ChatGPT for fixation object detection, eye contact detection, and conversation classification. In the experiments, significant correlations are observed between leadership skills and behavioral metrics, i.e., the output of our proposed methods, such as fixation time, transition patterns, and direct orders in speech. These results indicate that our proposed data collection and analysis framework can effectively solve skill assessment for training PICU teams.

Wensi Wu, Matthew Daemer, Jeffrey A. Weiss et al.

Valvular heart disease is prevalent and a major contributor to heart failure. Valve leaflet strain is a promising metric for evaluating the mechanics underlying the initiation and progression of valvular pathology. However, generalizable methods for noninvasively quantifying valvular strain from clinically acquired patient images remain limited. To address this limitation, we developed a geometric feature-tracking framework to quantify in vivo leaflet strain from 3DE images. The method integrates a cohort-derived geometric reference atlas to establish geometric correspondence and introduces a novel distance-weighted coherent point drift algorithm for non-rigid registration. We evaluated performance against a finite element benchmark model and compared the approach with conventional point-based tracking methods. The framework was applied to pediatric and adult patient datasets (N = 31) across variable valve morphologies. The proposed method demonstrated greater accuracy in quantifying anatomical alignment and leaflet strain than conventional point-based approaches. Validation against the finite element benchmark confirmed improved strain estimation. The framework achieved reliable inter-phase tracking of valve deformation across diverse morphologies in pediatric and adult patients. Analysis identified a consistent distribution pattern of the 1st principal strain associated with leaflet billow (prolapse). This feature-tracking framework provides a generalizable method for noninvasive quantification of atrioventricular valve leaflet strain from clinical 3DE images. Characterization of biomechanical strain patterns may improve prognostic assessment and support longitudinal evaluation of valvular heart disease. Further investigation of the biomechanical signatures of heart valve disease has the potential to enhance prognostic assessment and longitudinal evaluation of valvular heart disease.

Mahya Shabani, Parnian Shobeiri, Shadi Nouri et al.

Abstract Background Patients with autoimmune and immune-mediated diseases (AI-IMD) are at greater risk of COVID-19 infection; therefore, they should be prioritized in vaccination programs. However, there are concerns regarding the safety of COVID-19 vaccines in terms of disease relapse, flare, or exacerbation. In this study, we aimed to provide a more precise and reliable vision using systematic review and meta-analysis. Methods PubMed-MEDLINE, Embase, and Web of Science were searched for original articles reporting the relapse/flare in adult patients with AI-IMD between June 1, 2020 and September 25, 2022. Subgroup analysis and sensitivity analysis were conducted to investigate the sources of heterogeneity. Statistical analysis was performed using R software. Results A total of 134 observations of various AI-IMDs across 74 studies assessed the rate of relapse, flare, or exacerbation in AI-IMD patients. Accordingly, the crude overall prevalence of relapse, flare, or exacerbation was 6.28% (95% CI [4.78%; 7.95%], I 2 = 97.6%), changing from 6.28% (I 2 = 97.6%) to 6.24% (I 2 = 65.1%) after removing the outliers. AI-IMD patients administering mRNA, vector-based, and inactive vaccines showed 8.13% ([5.6%; 11.03%], I 2 = 98.1%), 0.32% ([0.0%; 4.03%], I 2 = 93.5%), and 3.07% ([1.09%; 5.9%], I 2 = 96.2%) relapse, flare, or exacerbation, respectively (p-value = 0.0086). In terms of disease category, nephrologic (26.66%) and hematologic (14.12%) disorders had the highest and dermatologic (4.81%) and neurologic (2.62%) disorders exhibited to have the lowest crude prevalence of relapse, flare, or exacerbation (p-value < 0.0001). Conclusion The risk of flare/relapse/exacerbation in AI-IMD patients is found to be minimal, especially with vector-based vaccines. Vaccination against COVID-19 is recommended in this population.

Mohaddeseh Badpeyma, Roghayeh Molani-Gol, Fatemeh Sistanian et al.

Background: This study aims to determine the effect of different GnRH agonist brands on body mass index (BMI), weight, and height in patients referred to the pediatric endocrinology clinic of Akbar Hospital.Methods: In this cohort study, 80 girls aged 5-8 years diagnosed with precocious puberty cases were included according to the Tanner staging and at the second puberty stage. The patients were classified into three groups of GnRH agonists, A, B, and C, receiving Diphereline, Microrelin, and Variopeptyl, respectively. Height, weight, and BMI were calculated every three months.Results: In group A, the weight (P=0.007) and BMI (P<0.001) percentiles and weight (P=0.024) and height (P=0.021) Z-scores were significantly increased compared to the baseline. In group B, the weight (P=0.024) and height (P=0.020) Z-scores also increased at the end of the study. However, the changes in group C were not significant. In addition, the weight, height, and BMI Z-scores were significantly increased in normal-weight subjects compared to overweight and obese participants. The results of comparing the changes in the weight and height between the three-drug groups showed no significant difference (P=0.142 and 0.161, respectively).Conclusion: The findings of this study revealed that GnRH agonists could increase height, weight, and BMI; however, this increase was not significant for one type of GnRH agonist. Future prospective long-term follow-up studies are required to elucidate whether GnRH treatment affects final adult weight and height and clarify the difference between various types of GnRH agonists among participants with diverse health statuses.

Ammar Ahmed, Ali Shariq Imran, Abdul Manaf et al.

Diagnosing and treating abnormalities in the wrist, specifically distal radius, and ulna fractures, is a crucial concern among children, adolescents, and young adults, with a higher incidence rate during puberty. However, the scarcity of radiologists and the lack of specialized training among medical professionals pose a significant risk to patient care. This problem is further exacerbated by the rising number of imaging studies and limited access to specialist reporting in certain regions. This highlights the need for innovative solutions to improve the diagnosis and treatment of wrist abnormalities. Automated wrist fracture detection using object detection has shown potential, but current studies mainly use two-stage detection methods with limited evidence for single-stage effectiveness. This study employs state-of-the-art single-stage deep neural network-based detection models YOLOv5, YOLOv6, YOLOv7, and YOLOv8 to detect wrist abnormalities. Through extensive experimentation, we found that these YOLO models outperform the commonly used two-stage detection algorithm, Faster R-CNN, in fracture detection. Additionally, compound-scaled variants of each YOLO model were compared, with YOLOv8m demonstrating a highest fracture detection sensitivity of 0.92 and mean average precision (mAP) of 0.95. On the other hand, YOLOv6m achieved the highest sensitivity across all classes at 0.83. Meanwhile, YOLOv8x recorded the highest mAP of 0.77 for all classes on the GRAZPEDWRI-DX pediatric wrist dataset, highlighting the potential of single-stage models for enhancing pediatric wrist imaging.

Nicholas Uhorchak, Ruben A. Proano, Sandra Eksioglu et al.

Effective and efficient scheduling of vaccine distribution can significantly impact vaccine uptake, which is critical to controlling the spread of infectious diseases. Ineffective scheduling can lead to waste, delays, and low vaccine coverage, potentially weakening the efforts to protect the public. Organizations such as UNICEF (United Nations Children's Fund), PAHO (Pan American Health Organization), and GAVI (Gavi, the Vaccine Alliance) coordinate vaccine tenders to ensure that enough supply is available on the international market at the lowest possible prices. Scheduling vaccine tenders over a planning horizon in a way that is equitable, efficient, and accessible is a complex problem that involves trade-offs between multiple objectives while ensuring that vaccine availability, demand, and logistical constraints are met. The current method for scheduling tenders is generally reactive and over short planning horizons. Vaccine tenders are scheduled when supply is insufficient to cover demand. We propose an optimization model to dynamically and proactively generate vaccine tender schedules over long planning horizons. This model helps us address the following research questions: What should the optimal sequencing and scheduling of vaccine tenders be to enhance affordability and profit over long time horizons? What is the optimal tender procurement schedule for single or multiple antigen scenarios? We use several real-life data sources to validate the model and address our research questions. Results from our analysis show when to schedule vaccine tenders, what volumes manufacturers should commit to, and the optimal tender lengths to satisfy demand. We show that vaccine tenders tend towards maximum lengths, generally converge over long time horizons, and are robust to changes in varying conditions.

Tahmim Hossain, Faisal Sayed, Yugesh Rai et al.

The "MedBike" is an innovative project in the field of pediatric cardiac rehabilitation. It is a 2D interactive game created specifically for children under the age of 18 who have cardiac conditions. This game is part of the MedBike system, a novel rehabilitation tool combining physical exercise with the spirit of gaming. The MedBike game provides children with a safe, controlled, and engaging environment in which to exercise and recover. It has three distinct levels of increasing intensity, each with its own set of environments and challenges that are tailored to different stages of rehabilitation. This report dives into the details of the MedBike game, highlighting its unique features and gameplay.

Zhifan Jiang, Daniel Capellán-Martín, Abhijeet Parida et al.

Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly important role in clinical diagnosis and prognosis. The International Brain Tumor Segmentation (BraTS) Challenge 2024 offers a unique benchmarking opportunity, including various types of brain tumors in both adult and pediatric populations, such as pediatric brain tumors (PED), meningiomas (MEN-RT) and brain metastases (MET), among others. Compared to previous editions, BraTS 2024 has implemented changes to substantially increase clinical relevance, such as refined tumor regions for evaluation. We propose a deep learning-based ensemble approach that integrates state-of-the-art segmentation models. Additionally, we introduce innovative, adaptive pre- and post-processing techniques that employ MRI-based radiomic analyses to differentiate tumor subtypes. Given the heterogeneous nature of the tumors present in the BraTS datasets, this approach enhances the precision and generalizability of segmentation models. On the final testing sets, our method achieved mean lesion-wise Dice similarity coefficients of 0.926, 0.801, and 0.688 for the whole tumor in PED, MEN-RT, and MET, respectively. These results demonstrate the effectiveness of our approach in improving segmentation performance and generalizability for various brain tumor types. The source code of our implementation is available at https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids. Additionally, an open-source web-application is accessible at https://segmenter.hope4kids.io/ which uses the docker container aparida12/brats-peds-2024:v20240913 .

Xenophon Sinopidis, Panagiotis Kallianezos, Constantinos Petropoulos et al.

Background: Head trauma is one of the most common pediatric emergencies. While the psychological effects of severe head injuries are well studied, the psychological consequences of mild head injuries often go overlooked. Head injuries with a Glasgow Coma Scale score of 13–15, with symptoms such as headache, vomiting, brief loss of consciousness, transient amnesia, and absence of focal neurological signs, are defined as mild. The aim of this study is to evaluate the stress of children with mild head injuries and their parents’ relevant perception during the early post-traumatic period. Methods: This is a prospective cross-sectional study on a cohort of children with mild head injuries and their parents. Two questionnaires were implemented, the Child Trauma Screening Questionnaire (CTSQ) which was compiled by the children, and the Children’s Revised Impact of Event Scale (CRIES-13), compiled by their parents. Both questionnaires are widely used and reliable. The first presents an excellent predictive ability in children with a risk of post-traumatic stress disorder, while the second is a weighted self-completed detecting instrument for the measurement of post-traumatic stress in children and adolescents, with a detailed evaluation of their reactions to the traumatic incident. The participants responded one week and one month after the traumatic event. Results: A total of 175 children aged 6–14 years and 174 parents participated in the study. Stress was diagnosed in 33.7% of children after one week, and in 9.9% after one month. Parental responses suggesting stress presence in their children were 19.0% and 3.9%, respectively. These outcomes showed that mild head injuries are not so innocent. They are often underestimated by their parents and may generate a psychological burden to the children during the early post-traumatic period. Conclusions: Mild head injuries may affect the emotional welfare of children. Healthcare providers should understand the importance of the psychological effect of this overlooked type of injury. They should be trained in the psychological effect of trauma and be aware of this probability, promptly notify the parents accordingly, and provide psychological assistance beyond medical treatment. Follow-up and support are needed to avoid the possibility of future post-traumatic stress disorder. More extensive research is needed as the outcomes of this study regarded a limited population in numbers, age, and survey period. Furthermore, many children with mild head injuries do not ever visit the emergency department and stay at home unrecorded. Community-based research on the topic should therefore be considered.

Amy Sisson, Emily Zientek, Sanika Rane et al.

Introduction While ensuring appropriate growth is essential for all children, optimising nutritional status in children with cystic fibrosis (CF) is critical for improving health outcomes. Nutritional challenges in CF are multifactorial and malnutrition is common. While gastrostomy tubes (G-tubes) can improve weight status in individuals with CF, they also have common and chronic complications resulting in clinical equipoise. To date, factors influencing G-tube decision-making among caregivers of children with CF have not been systematically explored. This review aims to chart existing knowledge about caregivers’ decisional needs related to G-tube placement, with a focus on caregivers of children with CF, as well as known medical and psychosocial benefits and risks of G-tube feedings in paediatric care.Methods and analysis This scoping review will follow the JBI methodological framework. We will include articles published between 1 January 1985 and 1 November 2023 in English and Spanish from MEDLINE (Ovid), Embase, CINAHL, PsycInfo, Cochrane Database of Systematic Reviews and Web of Science related to G-tube decision-making. Articles published in languages besides English and Spanish will be excluded. Articles will be screened for final eligibility and inclusion according to title and abstract, followed by full texts. Articles will be independently reviewed by two reviewers and any disagreements discussed with a third reviewer for consensus. We will map themes and concepts, and data extracted will be presented in tabular, diagrams and descriptive summaries.Ethics and dissemination As a form of secondary analysis, scoping reviews do not require ethics approval. This review will inform future research with caregivers involved in G-tube decision-making for children with CF. The final review will be submitted to a peer-reviewed scientific journal, disseminated at relevant academic conferences and will be shared with patients and clinicians.Trial registration number Center for Open Science. https://osf.io/g4pdb.

Yixing Huang, Ahmed Gomaa, Sabine Semrau et al.

The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. In this work, we evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology using the 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases. For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 63.65% and 74.57%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates better knowledge of statistics, CNS & eye, pediatrics, biology, and physics than knowledge of bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts. Both evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Because of the risk of hallucination, facts provided by ChatGPT always need to be verified.

Khaled Saab, Siyi Tang, Mohamed Taha et al.

A major barrier to deploying healthcare AI models is their trustworthiness. One form of trustworthiness is a model's robustness across different subgroups: while existing models may exhibit expert-level performance on aggregate metrics, they often rely on non-causal features, leading to errors in hidden subgroups. To take a step closer towards trustworthy seizure onset detection from EEG, we propose to leverage annotations that are produced by healthcare personnel in routine clinical workflows -- which we refer to as workflow notes -- that include multiple event descriptions beyond seizures. Using workflow notes, we first show that by scaling training data to an unprecedented level of 68,920 EEG hours, seizure onset detection performance significantly improves (+12.3 AUROC points) compared to relying on smaller training sets with expensive manual gold-standard labels. Second, we reveal that our binary seizure onset detection model underperforms on clinically relevant subgroups (e.g., up to a margin of 6.5 AUROC points between pediatrics and adults), while having significantly higher false positives on EEG clips showing non-epileptiform abnormalities compared to any EEG clip (+19 FPR points). To improve model robustness to hidden subgroups, we train a multilabel model that classifies 26 attributes other than seizures, such as spikes, slowing, and movement artifacts. We find that our multilabel model significantly improves overall seizure onset detection performance (+5.9 AUROC points) while greatly improving performance among subgroups (up to +8.3 AUROC points), and decreases false positives on non-epileptiform abnormalities by 8 FPR points. Finally, we propose a clinical utility metric based on false positives per 24 EEG hours and find that our multilabel model improves this clinical utility metric by a factor of 2x across different clinical settings.

Yiran E. Liu, Jillian Oto, John Will et al.

Carceral facilities are high-risk settings for COVID-19 transmission. Factors associated with COVID-19 vaccine acceptance and hesitancy among incarcerated individuals are poorly understood, especially among jail residents. Here, we conducted a retrospective review of electronic health record (EHR) data on COVID-19 vaccine uptake in custody and additionally administered a survey to assess reasons for vaccine hesitancy, sources of COVID-19 information, and medical mistrust among residents of four Northern California jails. We performed multivariate logistic regression to determine associations with vaccine acceptance. Of 2,564 jail residents offered a COVID-19 vaccine between March 19, 2021 and June 30, 2021, 1,441 (56.2%) accepted at least one dose. Among vaccinated residents, 497 (34.5%) had initially refused. Vaccine uptake was higher among older individuals, women, those with recent flu vaccination, and those living in shared housing. Among 509 survey respondents, leading reasons for vaccine hesitancy were concerns around side effects and suboptimal efficacy, with cost and the need for an annual booster being other hypothetical deterrents to vaccination. Vaccine hesitancy was also associated with mistrust of medical personnel in and out of jail, although this association varied by race/ethnicity. Television and friends/family were the most common and most trusted sources of COVID-19 information, respectively. Overall, vaccine acceptance was much lower among jail residents than the local and national general population. Interventions to increase vaccination rates in this setting should utilize accessible and trusted sources of information to address concerns about side effects and efficacy, while working to mitigate medical and institutional mistrust among residents.

Florin Bösch, Markus A. Landolt, Matthias R. Baumgartner et al.

Background: Living with a non-acute (phenylketonuria) or acute (e.g. urea cycle disorders, organic acidurias) intoxication-type inborn error of metabolism (IT-IEM) can have a substantial impact on health-related quality of life (HrQoL) of paediatric patients and their families. Parents take primary responsibility for treatment monitoring and experience worry and fear about their child's health status. Quantitative evidence on parental psychological factors which may influence the HrQoL of patients with IT-IEM are sparse to non-existent. Methods: In this multicenter survey study 50 parents of IT-IEM patients (ages 5–19) assessed the severity of their child's disease, reported on caregiver burden, and proxy-rated their child's HrQoL. Additionally, 35 patient self-reports on HrQoL were obtained (n = 16 female patients, n = 19 male patients). Multiple linear regressions were conducted to examine the predictive power of child age, sex, medical diagnosis type (acute / non-acute), parental perceived disease severity and caregiver burden on patients' HrQoL. Mediation analyses were used to investigate the relation of caregiver burden and parental ratings of disease severity with patients' HrQoL. Results: Significant regression models for self-reported [F(5,34) = 10.752, p < .001, R2 adj.. = 0.59] and parent proxy reported HrQoL [F(5,49) = 20.513, p < .001, R2 adj.. = 0.67] emerged. High caregiver burden and perceived disease severity predicted significantly lower patient self- and proxy-reported HrQoL while type of diagnosis (acute versus non-acute) did not. Female sex predicted significantly lower self-reported HrQoL. High caregiver burden was the mediating factor between high perceived severity of the child's disease and lower proxy- by parent rated HrQoL. Conclusion: Detecting elevated burden of care and providing support for parents seems crucial to prevent adverse consequences for their children's HrQoL. Intervention studies are needed, to assess which support programs are most efficient.

Natascia Bertoncelli, Licia Lugli, Luca Bedetti et al.

<b>Background</b>: The birth of a preterm infant and his/her immediate admittance to the Neonatal Intensive Care Unit (NICU) are sudden, unexpected, stressful and painful events for parents. In the last decade, in response to the increased awareness of the stressful experiences of parents, much attention has been paid to Family-Centered Care (FCC) and the implementation of the Newborn Individualized Developmental Care and Assessment Program (NIDCAP). According to the NIDCAP model, the infant–parents’ dyad is the core of the care provided by the NICU professionals to reduce the stress experienced by parents. So far, the literature does not show a clear correlation between parental experiences and the NICU practices according NIDCAP principles. <b>Aims</b>: To explore how parents of preterm infants experienced the NIDCAP-based care from admission to discharge, in particular, their relationships with NICU professionals and with other parents, and the organization of the couple’s daily activities during this process. <b>Design</b>: Qualitative exploratory study. <b>Methods</b>: Twelve parents of preterm infants born between January 2018 and December 2020 at the NICU of Modena, with a gestational age at birth of less than 30 weeks and/or a birth weight of less than 1250 g, were recruited. Three couples had twins, and the total number of infants was 15. All infants were followed for up to 24 months post-term age (PTA) for neurological outcomes. Each couple was given a semi-structured online interview about their experience during their infant’s hospitalization in the NICU up to discharge. The interview was developed around three time points: birth, hospitalization and discharge. The data analysis was conducted according to the template analysis method. <b>Results</b>: The admission to the NICU was unexpected and extraordinary, and its impact was contained by the skilled staff who were capable of welcoming the parents and making them feel they were involved and active collaborators in the care of their infant. The emotional experience was compared to being in a blender; they were overwhelmed by changing emotions, ranging from terrible fear to extreme joy. The couple’s activities of daily life were reorganized after the infant’s birth and admission to the NICU. Fathers felt unbalanced and alone in taking care of their partners and their children. <b>Conclusions</b>: This is the first study in Italy to explore parental experience in an NICU implementing NIDCAP-based care. The NIDCAP approach in the NICU of Modena helps parents to be involved early, to develop parental skills, and to be prepared for the transition home; and it also facilitates and enhances the relationship between parents and NICU staff.

Lívia Leite da Silva Macêdo, Juliana de Oliveira Freitas Miranda, Kátia Santana Freitas et al.

Objetivo: desenvolver e validar o conteúdo do Instrumento para Transferência do Cuidado de Enfermagem do paciente pediátrico. Métodos: estudo metodológico, desenvolvido em duas etapas, envolvendo 37 enfermeiros. A primeira etapa contemplou o desenvolvimento do instrumento. A segunda etapa foi a validação de conteúdo pelos experts, por meio da técnica Delphi e aplicação do teste piloto. Para análise dos dados foi empregado o Índice de Validade de Conteúdo. Resultados: o instrumento foi desenvolvido com quatro componentes e alcançou Índice de Validação de Conteúdo geral de 0,95. O teste piloto do instrumento foi aplicado em 25 transferências de cuidado pelas enfermeiras, que o consideraram aplicável ao contexto do estudo. Conclusão: o instrumento foi validado sob os aspectos da aparência/clareza, abrangência, pertinência e aplicabilidade à prática do enfermeiro no contexto hospitalar pediátrico estudado, sem demandar muito tempo para aplicação pelas enfermeiras.  

Wensi Wu, Stephen Ching, Steve A. Maas et al.

Atrioventricular valve regurgitation is a significant cause of morbidity and mortality in patients with acquired and congenital cardiac valve disease. Image-derived computational modeling of atrioventricular valves has advanced substantially over the last decade and holds particular promise to inform valve repair in small and heterogeneous populations which are less likely to be optimized through empiric clinical application. While an abundance of computational biomechanics studies have investigated mitral and tricuspid valve disease in adults, few studies have investigated application to vulnerable pediatric and congenital heart populations. Further, to date, investigators have primarily relied upon a series of commercial applications that are neither designed for image-derived modeling of cardiac valves, nor freely available to facilitate transparent and reproducible valve science. To address this deficiency, we aimed to build an open-source computational framework for the image-derived biomechanical analysis of atrioventricular valves. In the present work, we integrated an open-source valve modeling platform, SlicerHeart, and an open-source biomechanics finite element modeling software, FEBio, to facilitate image-derived atrioventricular valve model creation and finite element analysis. We present a detailed verification and sensitivity analysis to demonstrate the fidelity of this modeling in application to 3D echocardiography-derived pediatric mitral and tricuspid valve models. Our analyses achieved excellent agreement with those reported in the literature. As such, this evolving computational framework offers a promising initial foundation for future development and investigation of valve mechanics, in particular collaborative efforts targeting the development of improved repairs for children with congenital heart disease.

Aravinda Raman, Sameena Pathan, Tanweer Ali

Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. Manual processes are complicated and necessitate the expertise of experts. This is where deep learning comes into play. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the input data, and their mean average errors are compared and evaluated to see which model predicts the best.

Shashwat Kumar, Robert Gutierez, Debajyoti Datta et al.

Neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), cause progressive muscular degeneration and loss of motor function for 1 in 6,000 children. Traditional upper limb motor function assessments do not quantitatively measure patient-performed motions, which makes it difficult to track progress for incremental changes. Assessing motor function in children with neuromuscular disorders is particularly challenging because they can be nervous or excited during experiments, or simply be too young to follow precise instructions. These challenges translate to confounding factors such as performing different parts of the arm curl slower or faster (phase variability) which affects the assessed motion quality. This paper uses curve registration and shape analysis to temporally align trajectories while simultaneously extracting a mean reference shape. Distances from this mean shape are used to assess the quality of motion. The proposed metric is invariant to confounding factors, such as phase variability, while suggesting several clinically relevant insights. First, there are statistically significant differences between functional scores for the control and patient populations (p$=$0.0213$\le$0.05). Next, several patients in the patient cohort are able to perform motion on par with the healthy cohort and vice versa. Our metric, which is computed based on wearables, is related to the Brooke's score ((p$=$0.00063$\le$0.05)), as well as motor function assessments based on dynamometry ((p$=$0.0006$\le$0.05)). These results show promise towards ubiquitous motion quality assessment in daily life.