Hasil untuk "Pediatrics"

Mitchel J. Colebank, Alfonso Limon, Anthony Chang et al.

Computer modeling of the cardiovascular system has potential to revolutionize personalized medical care. This is especially promising for congenital heart defects, such as ventricular septal defect (VSD), a hole between the two ventricles of the heart. However, relatively few studies have built computer models for VSD, nor have they considered how natural adaptation to the cardiovascular system with age might interact with the presence of a small, medium, or large size VSD. Here, we combine a lumped parameter model of the cardiovascular system with two key modeling components: a size-dependent resistance dictating shunt flow between the two ventricles and age-dependent scaling relationships for the systemic and pulmonary circulations. Our results provide insight into changes in hemodynamic conditions with various VSD sizes. We investigate the combined effects of VSD size, vascular parameters, and age, showing distinct differences with these three factors. This study lays the necessary foundation for studying VSD and towards building digital shadows and digital twins for managing VSD in pediatrics.

Manasi Kanade, Abhi Thakkar, Gabriela Fernandes

Background: Pediatric dental disease remains one of the most prevalent and inequitable chronic health conditions worldwide. Although strong epidemiological evidence links oral health outcomes to socio-economic and demographic determinants, most artificial intelligence (AI) applications in dentistry rely on image-based diagnosis and black-box prediction models, limiting transparency and ethical applicability in pediatric populations. Objective: This study aimed to develop and evaluate an explainable machine learning framework for pediatric dental risk stratification that prioritizes interpretability, calibration, and ethical deployment over maximal predictive accuracy. Methods: A supervised machine learning model was trained using population-level pediatric data including age, income-to-poverty ratio, race/ethnicity, gender, and medical history. Model performance was assessed using receiver operating characteristic (ROC) analysis and calibration curves. Explainability was achieved using SHapley Additive exPlanations (SHAP) to provide global and individual-level interpretation of predictions. Results: The model achieved modest discrimination (AUC = 0.61) with conservative calibration, underestimating risk at higher probability levels. SHAP analysis identified age and income-to-poverty ratio as the strongest contributors to predicted risk, followed by race/ethnicity and gender. Conclusion: Explainable machine learning enables transparent, prevention-oriented pediatric dental risk stratification and supports population screening and equitable resource allocation rather than diagnostic decision-making.

Bin Liu, Ihtisham Bukhari, Fazhan Li et al.

Eleni Z. Giannopoulou, Stefanie Zorn, Melanie Schirmer et al.

Abstract Background Melanocortin 4 receptor (MC4R) deficiency is the most common monogenic cause of obesity, yet remains underdiagnosed. Patients with monogenic obesity often undergo a frustrating diagnostic and therapeutic odyssey of years of ineffective lifestyle interventions before a causal diagnosis is made. We report a four-generation family where genetic testing in a child identified a likely pathogenic MC4R variant also carried by three ancestors. Methods The studied family included a 7-year-old index patient, her mother, grandmother, and great-grandmother with a history of early-onset obesity. Panel sequencing of monogenic obesity genes was performed in the index patient whereas in the relatives targeted analysis of the familial MC4R variant was performed by Sanger sequencing. Results The index patient developed severe obesity by age 2 years, with hyperphagia, tall stature, and dyslipidemia. Despite lifestyle interventions, her body mass index (BMI) continued to increase. At the age of 7 years, genetic panel testing identified a rare monoallelic variant in the MC4R gene c.913C > T; p.Arg305Trp, previously shown to impair receptor function. Treatment with liraglutide (3.0 mg/day) was initiated at age 8 years, resulting in marked reduction in BMI during the first year of treatment. Subsequent genetic testing of family members identified the same variant in her mother, grandmother, and great-grandmother, all of whom had a history of early-onset obesity and related comorbidities, consistent with segregation of the variant within the family. Conclusions This case underscores the importance of early genetic testing in severe childhood obesity to avoid ineffective treatments and enable targeted therapies (e.g., GLP-1 analogues). Diagnosing (likely) pathogenic MC4R variants can also identify at-risk relatives, providing psychological and clinical benefits across generations.

Ilerioluwakiiye Abolade, Emmanuel Idoko, Solomon Odelola et al.

Pediatric chest X-ray imaging is essential for early diagnosis, particularly in low-resource settings where advanced imaging modalities are often inaccessible. Low-dose protocols reduce radiation exposure in children but introduce substantial noise that can obscure critical anatomical details. Conventional denoising methods often degrade fine details, compromising diagnostic accuracy. In this paper, we present SharpXR, a structure-aware dual-decoder U-Net designed to denoise low-dose pediatric X-rays while preserving diagnostically relevant features. SharpXR combines a Laplacian-guided edge-preserving decoder with a learnable fusion module that adaptively balances noise suppression and structural detail retention. To address the scarcity of paired training data, we simulate realistic Poisson-Gaussian noise on the Pediatric Pneumonia Chest X-ray dataset. SharpXR outperforms state-of-the-art baselines across all evaluation metrics while maintaining computational efficiency suitable for resource-constrained settings. SharpXR-denoised images improved downstream pneumonia classification accuracy from 88.8% to 92.5%, underscoring its diagnostic value in low-resource pediatric care.

Solomon Amenyo, Maura R. Grossman, Daniel G. Brown et al.

This study explores the integration of artificial intelligence (AI) or large language models (LLMs) into pediatric rehabilitation clinical documentation, focusing on the generation of SOAP (Subjective, Objective, Assessment, Plan) notes, which are essential for patient care. Creating complex documentation is time-consuming in pediatric settings. We evaluate the effectiveness of two AI tools; Copilot, a commercial LLM, and KAUWbot, a fine-tuned LLM developed for KidsAbility Centre for Child Development (an Ontario pediatric rehabilitation facility), in simplifying and automating this process. We focus on two key questions: (i) How does the quality of AI-generated SOAP notes based on short clinician summaries compare to human-authored notes, and (ii) To what extent is human editing necessary for improving AI-generated SOAP notes? We found no evidence of prior work assessing the quality of AI-generated clinical notes in pediatric rehabilitation. We used a sample of 432 SOAP notes, evenly divided among human-authored, Copilot-generated, and KAUWbot-generated notes. We employ a blind evaluation by experienced clinicians based on a custom rubric. Statistical analysis is conducted to assess the quality of the notes and the impact of human editing. The results suggest that AI tools such as KAUWbot and Copilot can generate SOAP notes with quality comparable to those authored by humans. We highlight the potential for combining AI with human expertise to enhance clinical documentation and offer insights for the future integration of AI into pediatric rehabilitation practice and other settings for the management of clinical conditions.

Maria Popa, Gabriela Adriana Visa

Demyelinating disorders of the central nervous system may have multiple causes, the most common are infections, autoimmune responses, genetic or vascular etiology. Demyelination lesions are characterized by areas were the myelin sheath of the nerve fibers are broken or destroyed. Among autoimmune disorders, Multiple Sclerosis (MS) is the most well-known Among these disorders, Multiple Sclerosis (MS) is the most well-known and aggressive form. Acute Disseminated Encephalomyelitis (ADEM) is another type of demyelinating disease, typically with a better prognosis. Magnetic Resonance Imaging (MRI) is widely used for diagnosing and monitoring disease progression by detecting lesions. While both adults and children can be affected, there is a significant lack of publicly available datasets for pediatric cases and demyelinating disorders beyond MS. This study introduces, for the first time, a publicly available pediatric dataset for demyelinating lesion segmentation. The dataset comprises MRI scans from 13 pediatric patients diagnosed with demyelinating disorders, including 3 with ADEM. In addition to lesion segmentation masks, the dataset includes extensive patient metadata, such as diagnosis, treatment, personal medical background, and laboratory results. To assess the quality of the dataset and demonstrate its relevance, we evaluate a state-of-the-art lesion segmentation model trained on an existing MS dataset. The results underscore the importance of diverse datasets

Md Ohiduzzaman Ovi, Maliha Sanjana, Fahad Fahad et al.

Pediatric dental segmentation is critical in dental diagnostics, presenting unique challenges due to variations in dental structures and the lower number of pediatric X-ray images. This study proposes a custom SegUNet model with a VGG19 backbone, designed explicitly for pediatric dental segmentation and applied to the Children's Dental Panoramic Radiographs dataset. The SegUNet architecture with a VGG19 backbone has been employed on this dataset for the first time, achieving state-of-the-art performance. The model reached an accuracy of 97.53%, a dice coefficient of 92.49%, and an intersection over union (IOU) of 91.46%, setting a new benchmark for this dataset. These results demonstrate the effectiveness of the VGG19 backbone in enhancing feature extraction and improving segmentation precision. Comprehensive evaluations across metrics, including precision, recall, and specificity, indicate the robustness of this approach. The model's ability to generalize across diverse dental structures makes it a valuable tool for clinical applications in pediatric dental care. It offers a reliable and efficient solution for automated dental diagnostics.

Fatemeh Hosseinabadi, Mohammad Mojtaba Rohani

Pediatric pneumonia remains a leading cause of morbidity and mortality in children worldwide. Timely and accurate diagnosis is critical but often challenged by limited radiological expertise and the physiological and procedural complexity of pediatric imaging. This study investigates the performance of state-of-the-art convolutional neural network (CNN) architectures ResNetRS, RegNet, and EfficientNetV2 using transfer learning for the automated classification of pediatric chest Xray images as either pneumonia or normal.A curated subset of 1,000 chest X-ray images was extracted from a publicly available dataset originally comprising 5,856 pediatric images. All images were preprocessed and labeled for binary classification. Each model was fine-tuned using pretrained ImageNet weights and evaluated based on accuracy and sensitivity. RegNet achieved the highest classification performance with an accuracy of 92.4 and a sensitivity of 90.1, followed by ResNetRS (accuracy: 91.9, sensitivity: 89.3) and EfficientNetV2 (accuracy: 88.5, sensitivity: 88.1).

Hafida Hamdache, Alexia Gazeu, Marion Gambart et al.

Neuroblastoma, is a highly heterogeneous pediatric tumour and is responsible for 15% of pediatric cancer-related deaths. The clinical outcomes can vary from spontaneous regression to high metastatic disease. This extracranial tumour arises from a neural crest-derived cell and can harbor different phenotypes. Its heterogeneity may result from variations in differentiation states influenced by genetic and epigenetic factors and individual patient characteristics. This leads downstream to disruption of homeostasis and a metabolic shift in response to the tumour needs. Nutrition can play a key role in influencing various aspects of a tumour behaviour. This review provides an in-depth exploration of the aetiology of neuroblastoma and the different avenues of disease progression, which can be targeted with individualized nutrition intervention strategies to improve the well-being of children and optimize clinical outcomes.

Stina Persson, Emma Haglund, Rebecca Mortazavi et al.

Background: Adolescent depression is a growing public health concern. Health promotion for this group requires an understanding of what health means in the context of depression. Given that parents play a central role in adolescents’ everyday lives, their perspectives on health for adolescents are important. The aim of this study was to describe parents’ perceptions of what health means for adolescents with depression. Methods: This qualitative study employed a phenomenographic approach. In-depth interviews were conducted with 28 parents of adolescents with depression. The analysis resulted in four categories, each comprising two sub-categories, reflecting variations in how health was perceived. Results: Parents described health for adolescents with depression in terms of taking initiative in everyday life and creating a daily structure through routines (navigating daily life with depression); experiencing belonging in supportive relationships and understanding one’s own value (building trust in self and others); having a positive outlook while facing struggles and finding pleasure in daily activities (experiencing joy despite depression); and balancing body and mind and maintaining healthy habits (supporting well-being despite depression). Conclusions: The findings provide new insights into how parents understand health for adolescents with depression. These perceptions may inform the development of supportive and health-promoting strategies tailored to adolescents’ and their families’ everyday challenges.

Chuhan Cheng, Liyan Zhang

Background The neutrophil-to-lymphocyte ratio (NLR) is a marker of systemic inflammation associated with various diseases including respiratory conditions. However, the relationship between NLR and asthma in the pediatric population remains underexplored. Purpose This study aimed to explore the association between NLR and asthma in children and adolescents and assess its potential role as a predictive biomarker for pediatric asthma. Methods We retrospectively analyzed the medical records of 12,974 children and adolescents from the National Health and Nutrition Examination Survey in 2011–2020. NLR was defined as the ratio of NLR counts. Asthma was diagnosed using a structured questionnaire. Multivariate logistic regression models were used to evaluate the association between NLR and asthma. A restricted cubic spline was used to explore nonlinear relationships, and a threshold analysis was conducted to identify potential cutoff values for the NLR. Results A total of 12,974 children and adolescents were included (male: 6,686 [51.5%]; mean [interquartile range] age, 10 [5.0–14.0 years]). After the adjustment for confounders, participants with the highest versus lowest NLR exhibited a significantly elevated risk of asthma (odds ratio [OR], 1.39; 95% confidence interval [CI], 1.13–1.71). Additionally, a multivariate restricted cubic spline analysis revealed a nonlinear relationship between NLR and asthma (P=0.023). A threshold analysis revealed that an NLR<2.23 was significantly associated with an increased risk of asthma (OR, 1.23; 95% CI, 1.05–1.45), while an NLR≥2.23 showed no significant association. A subgroup analysis revealed no interactive role of NLR and asthma. Conclusion Our findings indicate a nonlinear saturation-effect relationship between NLR and asthma in children and adolescents.

T. Williams, C. Bach, N. B. Matthiesen et al.

Many paediatric clinical research studies, whether observational or interventional, have as an eventual aim the identification or quantification of causal relationships. One might ask: does screen time influence childhood obesity? Could overuse of paracetamol in infancy cause wheeze? How does breastfeeding affect later cognitive outcomes? In this review, we present causal directed acyclic graphs (DAGs) to a paediatric audience. DAGs are a graphical tool which provide a way to visually represent and better understand the key concepts of exposure, outcome, causation, confounding, and bias. We use clinical examples, including those outlined above, framed in the language of DAGs, to demonstrate their potential applications. We show how DAGs can be most useful in identifying confounding and sources of bias, demonstrating inappropriate statistical adjustments for presumed biases, and understanding threats to validity in randomised controlled trials. We believe that a familiarity with DAGs, and the concepts underlying them, will be of benefit both to the researchers planning studies, and practising clinicians interpreting them.

G. Saggese, F. Vierucci, F. Prodam et al.

Vitamin D plays a pivotal role in the regulation of calcium-phosphorus metabolism, particularly during pediatric age when nutritional rickets and impaired bone mass acquisition may occur.Besides its historical skeletal functions, in the last years it has been demonstrated that vitamin D directly or indirectly regulates up to 1250 genes, playing so-called extraskeletal actions. Indeed, recent data suggest a possible role of vitamin D in the pathogenesis of several pathological conditions, including infectious, allergic and autoimmune diseases. Thus, vitamin D deficiency may affect not only musculoskeletal health but also a potentially wide range of acute and chronic conditions. At present, the prevalence of vitamin D deficiency is high in Italian children and adolescents, and national recommendations on vitamin D supplementation during pediatric age are lacking. An expert panel of the Italian Society of Preventive and Social Pediatrics reviewed available literature focusing on randomized controlled trials of vitamin D supplementation to provide a practical approach to vitamin D supplementation for infants, children and adolescents.

Sheng Cheng, Zbigniew A. Starosolski, Devika Subramanian

Recent advancements in deep learning for Medical Artificial Intelligence have demonstrated that models can match the diagnostic performance of clinical experts in adult chest X-ray (CXR) interpretation. However, their application in the pediatric context remains limited due to the scarcity of large annotated pediatric image datasets. Additionally, significant challenges arise from the substantial variability in pediatric CXR images across different hospitals and the diverse age range of patients from 0 to 18 years. To address these challenges, we propose SCC, a novel approach that combines transfer learning with self-supervised contrastive learning, augmented by an unsupervised contrast enhancement technique. Transfer learning from a well-trained adult CXR model mitigates issues related to the scarcity of pediatric training data. Contrastive learning with contrast enhancement focuses on the lungs, reducing the impact of image variations and producing high-quality embeddings across diverse pediatric CXR images. We train SCC on one pediatric CXR dataset and evaluate its performance on two other pediatric datasets from different sources. Our results show that SCC's out-of-distribution (zero-shot) performance exceeds regular transfer learning in terms of AUC by 13.6% and 34.6% on the two test datasets. Moreover, with few-shot learning using 10 times fewer labeled images, SCC matches the performance of regular transfer learning trained on the entire labeled dataset. To test the generality of the framework, we verify its performance on three benchmark breast cancer datasets. Starting from a model trained on natural images and fine-tuned on one breast dataset, SCC outperforms the fully supervised learning baseline on the other two datasets in terms of AUC by 3.6% and 5.5% in zero-shot learning.

Dingkang Yang, Jinjie Wei, Dongling Xiao et al.

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct doctor downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. Our model and dataset will be open-source for community development.

Mohammad Zunaed, Anwarul Hasan, Taufiq Hasan

Despite the advancement of deep learning-based computer-aided diagnosis (CAD) methods for pneumonia from adult chest x-ray (CXR) images, the performance of CAD methods applied to pediatric images remains suboptimal, mainly due to the lack of large-scale annotated pediatric imaging datasets. Establishing a proper framework to leverage existing adult large-scale CXR datasets can thus enhance pediatric pneumonia detection performance. In this paper, we propose a three-branch parallel path learning-based framework that utilizes both adult and pediatric datasets to improve the performance of deep learning models on pediatric test datasets. The paths are trained with pediatric only, adult only, and both types of CXRs, respectively. Our proposed framework utilizes the multi-positive contrastive loss to cluster the classwise embeddings and the embedding similarity loss among these three parallel paths to make the classwise embeddings as close as possible to reduce the effect of domain shift. Experimental evaluations on open-access adult and pediatric CXR datasets show that the proposed method achieves a superior AUROC score of 0.8464 compared to 0.8348 obtained using the conventional approach of join training on both datasets. The proposed approach thus paves the way for generalized CAD models that are effective for both adult and pediatric age groups.

Fatima Al Dhaheri, Jens Thomsen, Dean Everett et al.

The United Arab Emirates has very little data on the incidence or prevalence of fungal diseases. Using total and underlying disease risk populations and likely affected proportions, we have modelled the burden of fungal disease for the first time. The most prevalent serious fungal conditions are recurrent vulvovaginitis (~190,000 affected) and fungal asthma (~34,000 affected). Given the UAE’s low prevalence of HIV, we estimate an at-risk population of 204 with respect to serious fungal infections with cryptococcal meningitis estimated at 2 cases annually, 15 cases of <i>Pneumocystis</i> pneumonia (PCP) annually, and 20 cases of esophageal candidiasis in the HIV population. PCP incidence in non-HIV patients is estimated at 150 cases annually. Likewise, with the same low prevalence of tuberculosis in the country, we estimate a total chronic pulmonary aspergillosis prevalence of 1002 cases. The estimated annual incidence of invasive aspergillosis is 505 patients, based on local data on rates of malignancy, solid organ transplantation, and chronic obstructive pulmonary disease (5.9 per 100,000). Based on the 2022 annual report of the UAE’s national surveillance database, candidaemia annual incidence is 1090 (11.8/100,000), of which 49.2% occurs in intensive care. Fungal diseases affect ~228,695 (2.46%) of the population in the UAE.

G. Valerio, C. Maffeis, G. Saggese et al.

The Italian Consensus Position Statement on Diagnosis, Treatment and Prevention of Obesity in Children and Adolescents integrates and updates the previous guidelines to deliver an evidence based approach to the disease. The following areas were reviewed: (1) obesity definition and causes of secondary obesity; (2) physical and psychosocial comorbidities; (3) treatment and care settings; (4) prevention.The main novelties deriving from the Italian experience lie in the definition, screening of the cardiometabolic and hepatic risk factors and the endorsement of a staged approach to treatment. The evidence based efficacy of behavioral intervention versus pharmacological or surgical treatments is reported. Lastly, the prevention by promoting healthful diet, physical activity, sleep pattern, and environment is strongly recommended since the intrauterine phase.