Hasil untuk "Pediatrics"

V. Blanchette, Nigel S. Key, R. Ljung et al.

Seungjun Yi, Joakim Nguyen, Huimin Xu et al.

Thematic analysis (TA) is widely used in health research to extract patterns from patient interviews, yet manual TA faces challenges in scalability and reproducibility. LLM-based automation can help, but existing approaches produce codebooks with limited generalizability and lack analytic auditability. We present an automated TA framework combining iterative codebook refinement with full provenance tracking. Evaluated on five corpora spanning clinical interviews, social media, and public transcripts, the framework achieves the highest composite quality score on four of five datasets compared to six baselines. Iterative refinement yields statistically significant improvements on four datasets with large effect sizes, driven by gains in code reusability and distributional consistency while preserving descriptive quality. On two clinical corpora (pediatric cardiology), generated themes align with expert-annotated themes.

Hunter M Beach, Devin Jay D San Nicolas, Carly Miller et al.

Motor challenges are prevalent among autistic children, and games are able to simultaneously produce clinically meaningful results and provide a motivating context, but many current solutions are too rigid. We conducted a two-phase qualitative study comprised of semi-structured interviews and participatory design workshops with 7 pediatric physical and 5 occupational therapists (PTs/OTs) to investigate their perspectives and experiences with game and play-based interventions. We identified 8 prominent themes describing key characteristics of current successful interventions, opportunities, and barriers to adoption in clinical practice. We present a speculative design informed by thematic analysis that addresses current challenges of rigidity in Serious Games for Health (SG4H). Our modular platform (AutMotion Studio) hosts a suite of interventions as customizable minigames, allowing community members to contribute to and employ Wizard of Oz paradigms for flexible appropriation strategies.

Ray-Yuan Chung, Xuhai Xu, Ari Pollack

Large language model based health agents are increasingly used by health consumers and clinicians to interpret health information and guide health decisions. However, most AI systems in healthcare operate in siloed configurations, supporting individual users rather than the multi-stakeholder relationships central to healthcare. Such use can fragment understanding and exacerbate misalignment among patients, caregivers, and clinicians. We reframe AI not as a standalone assistant, but as a collaborator embedded within multi-party care interactions. Through a clinically validated fictional pediatric chronic kidney disease case study, we show that breakdowns in adherence stem from fragmented situational awareness and misaligned goals, and that siloed use of general-purpose AI tools does little to address these collaboration gaps. We propose a conceptual framework for designing AI collaborators that surface contextual information, reconcile mental models, and scaffold shared understanding while preserving human decision authority.

Dohyun Chun, Hae Woon Jung, Jongho Kang et al.

This study developed an accurate artificial intelligence model for predicting future height in children and adolescents using anthropometric and body composition data from the GP Cohort Study (588,546 measurements from 96,485 children aged 7-18). The model incorporated anthropometric measures, body composition, standard deviation scores, and growth velocity parameters, with performance evaluated using RMSE, MAE, and MAPE. Results showed high accuracy with males achieving average RMSE, MAE, and MAPE of 2.51 cm, 1.74 cm, and 1.14%, and females showing 2.28 cm, 1.68 cm, and 1.13%, respectively. Explainable AI approaches identified height SDS, height velocity, and soft lean mass velocity as crucial predictors. The model generated personalized growth curves by estimating individual-specific height trajectories, offering a robust tool for clinical decision support, early identification of growth disorders, and optimization of growth outcomes.

Jiani Tan, Kaili Wu, Jingjing Ma

Abstract Background This study sampled Chinese families of children with acute leukemia to assess their levels of anxiety and explore whether those levels were associated with social support levels and coping style. The study also aimed to identify demographic factors influencing anxiety, social support and coping style. Methods A purposive sample of 223 families whose children were being treated for acute leukemia at West China Second University Hospital, Sichuan University completed a questionnaire to provide basic demographic information as well as the Self-Rating Anxiety Scale, Social Support Rating Scale, and Simplified Coping Style Questionnaire. Categorical data were reported as n (%), while continuous data were reported as mean ± standard deviation if normally distributed or as median (interquartile range) if skewed. the Kruskal-Wallis or Wilcoxon rank sum test, Bonferroni correction for multiple comparisons, Pearson correlation analysis or Spearman correlation analysis were used for data analysis. Results The score of anxiety in our sample was 36.80 ± 9.05 points and 33.6% of family members exhibited clinically significant symptoms of anxiety; anxiety level was significantly higher in family members whose affected children had no siblings (P < 0.05). The score of social support was 42.86 ± 7.80 points. Objective social support level was significantly higher for families living in urban areas than rural areas (P < 0.05), and it correlated positively with monthly household income (B = 2.176, P = 0.009). The score of coping style was 50.23 ± 10.04 points. Coping style score was significantly higher for family members in urban areas, with more education, or with higher monthly household income. It also correlated positively with overall social support score. Conclusions Our results suggest that many families of children with acute leukemia suffer clinically significant anxiety. Families from rural areas and those with lower income have relatively poor social support and coping abilities. Additionally, levels of education is related to their coping abilities, and they can benefit from education and psychological support.

Shan Gao, Dahai Wang, Xingmei Ding et al.

BackgroundThe Wilms Tumour gene 1 (WT1, NM_024426.6) holds significant importance in the developmental processes of the kidneys and gonads. Herein, we report a case of nephrotic syndrome and differences of sex development in a patient with novel mutations in WT1 gene.MethodsThe child, identified as female based on social gender, exhibited symptoms at 6 years of age and was diagnosed with steroid-resistant nephrotic syndrome (SRNS). Renal biopsy findings indicated focal segmental glomerulosclerosis. Whole-exome sequencing unveiled a novel variant, c.1447 + 6(IVS9)T &gt; C, in the WT1 gene, and karyotypic analysis revealed 46, XY, aligning with the phenotypic presentation of Frasier syndrome (FS, OMIM#136680) associated with WT1 gene mutation. The influence of gene variants on mRNA splicing was examined using in vitro minigene assays.ResultsThe variant was classified as likely pathogenic (PS2 + PM2_Supporting + PP3) in accordance with American College of Medical Genetics and Genomics (ACMG) guidelines. in vitro minigene experiments demonstrated that the c.1447 + 6(IVS9)T &gt; C variant altered the splicing pattern of exon 9 in the WT1 gene from two isoforms to a single form, thereby supporting its pathogenicity.ConclusionThrough high-throughput sequencing and in vitro minigene splicing experiments, the c.1447 + 6T &gt; C variant in the WT1 gene was supported as the underlying genetic cause in the child patient, thereby expanding the spectrum of gene variants of WT1 gene and enhancing our comprehension of the molecular pathogenesis of this disorder.

Rui-Yang Ju, Chun-Tse Chien, Jen-Shiun Chiang

Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, surgeons often request patients to undergo X-ray imaging first, and prepare for the surgery based on the analysis of the X-ray images. With the development of neural networks, You Only Look Once (YOLO) series models have been widely used in fracture detection for Computer-Assisted Diagnosis, where the YOLOv8 model has obtained the satisfactory results. Applying the attention modules to neural networks is one of the effective methods to improve the model performance. This paper proposes YOLOv8-ResCBAM, which incorporates Convolutional Block Attention Module integrated with resblock (ResCBAM) into the original YOLOv8 network architecture. The experimental results on the GRAZPEDWRI-DX dataset demonstrate that the mean Average Precision calculated at Intersection over Union threshold of 0.5 (mAP 50) of the proposed model increased from 63.6% of the original YOLOv8 model to 65.8%, which achieves the state-of-the-art performance. The implementation code is available at https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8.

Abdesselam Ferdi

Computer-aided diagnosis (CAD) systems have greatly improved the interpretation of medical images by radiologists and surgeons. However, current CAD systems for fracture detection in X-ray images primarily rely on large, resource-intensive detectors, which limits their practicality in clinical settings. To address this limitation, we propose a novel lightweight CAD system based on the YOLO detector for fracture detection. This system, named ghost convolution-based YOLOv11 (G-YOLOv11), builds on the latest version of the YOLO detector family and incorporates the ghost convolution operation for feature extraction. The ghost convolution operation generates the same number of feature maps as traditional convolution but requires fewer linear operations, thereby reducing the detector's computational resource requirements. We evaluated the performance of the proposed G-YOLOv11 detector on the GRAZPEDWRI-DX dataset, achieving an mAP@0.5 of 0.535 with an inference time of 2.4 ms on an NVIDIA A10 GPU. Compared to the standard YOLOv11l, G-YOLOv11l achieved reductions of 13.6% in mAP@0.5 and 68.7% in size. These results establish a new state-of-the-art benchmark in terms of efficiency, outperforming existing detectors. Code and models are available at https://github.com/AbdesselamFerdi/G-YOLOv11.

Chun-Tse Chien, Rui-Yang Ju, Kuang-Yi Chou et al.

Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, surgeons often request patients to undergo X-ray imaging first and prepare for it based on the analysis of the radiologist. With the development of neural networks, You Only Look Once (YOLO) series models have been widely used in fracture detection as computer-assisted diagnosis (CAD). In 2023, Ultralytics presented the latest version of the YOLO models, which has been employed for detecting fractures across various parts of the body. Attention mechanism is one of the hottest methods to improve the model performance. This research work proposes YOLOv8-AM, which incorporates the attention mechanism into the original YOLOv8 architecture. Specifically, we respectively employ four attention modules, Convolutional Block Attention Module (CBAM), Global Attention Mechanism (GAM), Efficient Channel Attention (ECA), and Shuffle Attention (SA), to design the improved models and train them on GRAZPEDWRI-DX dataset. Experimental results demonstrate that the mean Average Precision at IoU 50 (mAP 50) of the YOLOv8-AM model based on ResBlock + CBAM (ResCBAM) increased from 63.6% to 65.8%, which achieves the state-of-the-art (SOTA) performance. Conversely, YOLOv8-AM model incorporating GAM obtains the mAP 50 value of 64.2%, which is not a satisfactory enhancement. Therefore, we combine ResBlock and GAM, introducing ResGAM to design another new YOLOv8-AM model, whose mAP 50 value is increased to 65.0%. The implementation code for this study is available on GitHub at https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8.

Jialin Wen, Qianqian Jiang, Lijun Yang et al.

Background: Hypoxic-ischemic injury of neurons is a pathological process observed in several neurological conditions, including ischemic stroke and neonatal hypoxic-ischemic brain injury (HIBI). An optimal treatment strategy for these conditions remains elusive. The present study delved deeper into the molecular alterations occurring during the injury process in order to identify potential therapeutic targets. Methods: Oxygen-glucose deprivation/reperfusion (OGD/R) serves as an established in vitro model for the simulation of HIBI. This study utilized RNA sequencing to analyze rat primary hippocampal neurons that were subjected to either 0.5 or 2 h of OGD, followed by 0, 9, or 18 h of reperfusion. Differential expression analysis was conducted to identify genes dysregulated during OGD/R. Time-series analysis was used to identify genes exhibiting similar expression patterns over time. Additionally, functional enrichment analysis was conducted to explore their biological functions, and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Quantitative real-time polymerase chain reaction (qRT-PCR) was used for validation of hub-gene expression. Results: The study included a total of 24 samples. Analysis revealed distinct transcriptomic alterations after OGD/R processes, with significant dysregulation of genes such as Txnip, Btg2, Egr1 and Egr2. In the OGD process, 76 genes, in two identified clusters, showed a consistent increase in expression; functional analysis showed involvement of inflammatory responses and signaling pathways like tumor necrosis factor (TNF), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and interleukin 17 (IL-17). PPI network analysis suggested that Ccl2, Jun, Cxcl1, Ptprc, and Atf3 were potential hub genes. In the reperfusion process, 274 genes, in three clusters, showed initial upregulation followed by downregulation; functional analysis suggested association with apoptotic processes and neuronal death regulation. PPI network analysis identified Esr1, Igf-1, Edn1, Hmox1, Serpine1, and Spp1 as key hub genes. qRT-PCR validated these trends. Conclusions: The present study provides a comprehensive transcriptomic profile of an in vitro OGD/R process. Key hub genes and pathways were identified, offering potential targets for neuroprotection after hypoxic ischemia.

Juming Xiong, Yilin Liu, Ruining Deng et al.

Eosinophilic Esophagitis (EoE) is a chronic, immune/antigen-mediated esophageal disease, characterized by symptoms related to esophageal dysfunction and histological evidence of eosinophil-dominant inflammation. Owing to the intricate microscopic representation of EoE in imaging, current methodologies which depend on manual identification are not only labor-intensive but also prone to inaccuracies. In this study, we develop an open-source toolkit, named Open-EoE, to perform end-to-end whole slide image (WSI) level eosinophil (Eos) detection using one line of command via Docker. Specifically, the toolkit supports three state-of-the-art deep learning-based object detection models. Furthermore, Open-EoE further optimizes the performance by implementing an ensemble learning strategy, and enhancing the precision and reliability of our results. The experimental results demonstrated that the Open-EoE toolkit can efficiently detect Eos on a testing set with 289 WSIs. At the widely accepted threshold of >= 15 Eos per high power field (HPF) for diagnosing EoE, the Open-EoE achieved an accuracy of 91%, showing decent consistency with pathologist evaluations. This suggests a promising avenue for integrating machine learning methodologies into the diagnostic process for EoE. The docker and source code has been made publicly available at https://github.com/hrlblab/Open-EoE.

Ipsita Sahin, Mehrnoosh Ayazi, Caio Mucchiani et al.

This paper focuses on the design and systematic evaluation of fabric-based, bellow-type soft pneumatic actuators to assist with flexion and extension of the elbow, intended for use in infant wearable devices. Initially, the performance of a range of actuator variants was explored via simulation. The actuator variants were parameterized based on the shape, number, and size of the cells present. Subsequently, viable actuator variants identified from the simulations were fabricated and underwent further testing on a physical model based on an infant's body anthropometrics. The performance of these variants was evaluated based on kinematic analyses using metrics including movement smoothness, path length, and elbow joint angle. Internal pressure of the actuators was also attained. Taken together, results reported herein provide valuable insights about the suitability of several actuator designs to serve as components for pediatric wearable assistive devices.

Palak Handa, Sakshi Tiwari, Nidhi Goel

More than 50 million individuals are affected by epilepsy, a chronic neurological disorder characterized by unprovoked, recurring seizures and psychological symptoms. Researchers are working to automatically detect or predict epileptic episodes through Electroencephalography (EEG) signal analysis, and machine, and deep learning methods. Good quality, open-source, and free EEG data acts as a catalyst in this ongoing battle to manage this disease. This article presents 40+ publicly available EEG datasets for adult and pediatric human populations from 2001-2023. A comparative analysis and discussion on open and private EEG datasets have been done based on objective parameters in this domain. Bonn and CHB-MIT remain the benchmark datasets used for the automatic detection of epileptic and seizure EEG signals. Meta-data has also been released for large EEG data like CHB-MIT. This article will be updated every year to report the progress and changing trends in the development of EEG datasets in this field.

Guoqi Yu, Hailun Yao, Huan Zheng et al.

Outbreaks of hand-foot-and-mouth disease(HFMD) have been associated with significant morbidity and, in severe cases, mortality. Accurate forecasting of daily admissions of pediatric HFMD patients is therefore crucial for aiding the hospital in preparing for potential outbreaks and mitigating nosocomial transmissions. To address this pressing need, we propose a novel transformer-based model with a U-net shape, utilizing the patching strategy and the joint prediction strategy that capitalizes on insights from herpangina, a disease closely correlated with HFMD. This model also integrates representation learning by introducing reconstruction loss as an auxiliary loss. The results show that our U-net Patching Time Series Transformer (UPTST) model outperforms existing approaches in both long- and short-arm prediction accuracy of HFMD at hospital-level. Furthermore, the exploratory extension experiments show that the model's capabilities extend beyond prediction of infectious disease, suggesting broader applicability in various domains.

Xiaofang Liu, Pan Zhang, Meiping Chen et al.

Objectives: The spread of SARS-Cov-2 remains a global concern along with the emergence of variants. This study aims to characterize the epidemiological and clinical features of hospitalized patients who were dragonized with five different variants of SARS-CoV-2 during the past 3 years. Methods: This retrospective study recruited 432 COVID-19 patients who were hospitalized in the First Hospital of Changsha from January 2020 to August 2022. Clinical records on clinical symptoms, laboratory profiles, and chest CT images was collected. The epidemiological and clinical features were compared between COVID-19 patients infected with either the wild-type, Omicron variant or pre- Omicron variants (e.g., Alpha, Beta, Delta). Results: A total of 432 laboratory-confirmed COVID-19 inpatients were dialogized during three waves, including 247 cases during the wild-type transmission period, 65 cases during the transmission period of pre-Omicron variants, and 119 cases during the transmission period of Omicron variants. The proportion of moderately or severely ill inpatients showed a gradual decline from the wild-type transmission period to the Omicron transmission period. The common symptoms of inpatients infected with SARS-CoV-2 wildtype strains included fever (67.61 %), cough (57.89 %), fatigue (33.60 %), and shortness of breath (12.15 %). In contrast, patients infected with other variants mostly showed upper respiratory symptoms. Based on chest CT images, a lower degree of acute pulmonary infection was observed among inpatients infected with the Omicron variants than those infected with the wild-type strain (31.09 % vs 93.12 %, p-value<0.01). Conclusions: Compared with the wild-type strain, SARS-CoV-2 variants of concern, especially the Omicron variant, mostly caused a lower degree of acute pulmonary infection, indicating the reduced disease severity and mortality among hospitalized COVID-19 patients.

Stephanie T. Aronson, Mahmut Y. Celiker, Ludovico Guarini et al.

Abstract Background Parvovirus is a common childhood infection that could be very dangerous to the fetus, if pregnant women become infected. The spectrum of effects range from pure red blood cell aplasia with hydrops fetalis to meningoencephalitis, with many symptoms in between. Severe anemia in the setting of pure red blood cell aplasia is one of the more common effects that neonatal experience (if infected intrapartum), with the current gold standard treatment being intrauterine or postnatal packed red blood cell (PRBC) transfusions, yet intravenous immunoglobulin (IVIG) may be a superior treatment option. Case presentation A preterm infant was born at 26th week of gestational age via emergency Cesarean section due to hydrops fetalis, with parvovirus B19 exposure one month prior. The infant tested positive for IgM antibodies against parvovirus B19. Among many other serious complications of both hydrops fetalis and premature delivery, the infant had severe unremitting anemia, and received many PRBC transfusion over the course of his 71-day-long neonatal intensive care unit stay. During a follow up appointments as outpatient, his blood tests showed persistent high copies of parvovirus B19. He was then supported with PRBC transfusions and treated with IVIG. After three doses of IVIG, the infant’s parvovirus B19 viral copy numbers have dramatically reduced and the infant did not require any more PRBC transfusions. Conclusions IVIG infusion effectively treated the parvovirus B19 infection and restored erythropoiesis making the child transfusion independent. Furthermore, since IVIG is safe and readily crosses the placenta, further studies are needed to determine if IVIG should be considered as an alternative prenatal treatment for congenital parvovirus B19 infection.

Francesco Coccia, Angelo Pietrobelli, Thomas Zoller et al.

Osteogenesis Imperfecta (OI) is a heterogeneous group of inherited skeletal dysplasias characterized by bone fragility. The study of bone metabolism, in these disease, is problematic in terms of clinical and genetic variability. The aims of our study were to evaluate the importance of Vitamin D levels in OI bone metabolism, reviewing studies performed on this topic and providing advice reflecting our experience using vitamin D supplementation. A comprehensive review on all English-language articles was conducted in order to analyze the influence of vitamin D in OI bone metabolism in pediatric patients. Reviewing the studies, contradictory data were found on the relationship between 25OH vitamin D levels and bone parameters in OI, and in several studies the baseline levels of 25OH D were below the threshold value of 75 nmol/L. In conclusion, according to the literature and to our experience, we highlight the importance of adequate vitamin D supplementation in children with OI.

Mansur Zhussupbekov, Greg W Burgreen, Jeongho Kim et al.

PediaFlow is a miniature mixed-flow ventricular assist device for neonates and toddlers. PediaFlow has a fully magnetically levitated rotor which improves biocompatibility, but the increased length of the rotor creates a long annular passage where fluid energy is lost. Therefore, a set of helical stator blades was proposed immediately after the impeller stage to remove the swirling flow and recover the dynamic head as static pressure. Automated computational fluid dynamics (CFD) shape optimization of the stator blades was performed to maximize pressure recovery at the operating point of 1.5 LPM and 16,000 RPM. Additionally, the effect on hemolysis and thrombogenicity was assessed using numerical modeling. The optimization algorithm favored fewer blades of greater length over a larger number of short blades. The ratio of wrap angle to axial length emerged as a key constraint to ensure the viability of a design. The best design had 2 blades and generated 73 mmHg of pressure recovery in an isolated stage. When re-introduced to the CFD simulation of the complete flow path, the added stator stage increased the pump head by 46% and improved the pump efficiency from 21.9% to 25.7% at the selected operating point. Automated CFD shape optimization combined with in silico evaluation of hemocompatibility can be an effective tool for exploring design choices and informing early development process.

Zihan Zhang, Xiang Xiang, Xuehua Peng et al.

Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination of tumor samples. This is time-consuming and may have a hidden impact on children. To handle this problem, we adopt multiple machine learning (ML) algorithms to predict the presence or absence of MYCN gene amplification. The dataset is composed of retrospective CT images of 23 neuroblastoma patients. Different from previous work, we develop the algorithm without manually-segmented primary tumors which is time-consuming and not practical. Instead, we only need the coordinate of the center point and the number of tumor slices given by a subspecialty-trained pediatric radiologist. Specifically, CNN-based method uses pre-trained convolutional neural network, and radiomics-based method extracts radiomics features. Our results show that CNN-based method outperforms the radiomics-based method.