搜索结果：Paediatrics

This study investigates the relationship between longitudinal serum creatinine measurements and the risk of adverse kidney outcomes in paediatric patients with auto-immune disorders at Great Ormond Street Hospital for Children NHS Foundation Trust, London. To jointly analyse repeated biomarker measurements and time-to-event outcomes, we employed a joint modelling framework that combines the creatinine trajectories with the time to death or diagnosis of acute kidney injury or chronic kidney disease. Covariates considered in analysis included demographic and clinical characteristics. The results demonstrate a strong association between evolving creatinine profiles and the risk of the composite event. Specifically, treatment with corticosteroids and calcium channel blockers was associated with an increased event risk, whereas immunosuppressive therapy was associated with a reduced risk. The longitudinal component showed that creatinine trajectories were significantly influenced by age and BMI z-score. To demonstrate the practical utility of the proposed framework, dynamic risk predictions were generated using patients' observed creatinine trajectories. Model performance was compared u

Measuring the angle between bone structures is a routine task in medical image analysis and provides a key quantitative parameter for diagnosis and treatment planning. Automated methods can reduce time and cost while improving reproducibility. In this work, we address automatic bone pose estimation using a learning-based point candidate proposal followed by a line model to extract axis parameters. Since conventional line models such as least squares are sensitive to outliers, we incorporate false-positive reduction strategies and robust fitting techniques, such as RANSAC and Hough transforms, to improve robustness. We evaluate our method on three clinically relevant paediatric angle estimation tasks: fracture fragment assessment in radiographs and ultrasound and developmental dysplasia of the hip evaluation in ultrasound using the Graf method. Our approach achieves mean errors of $4.1^\circ$, $5.4^\circ$, and $5.51^\circ$, respectively, not only remaining within the expected clinical observer variability, but also significantly outperforming landmark-based methods. Our code and annotations for fracture angle assessment in radiographs are publicly available on GitHub.

Automated stuttering detection (ASD) systems struggle with paediatric speech due to high acoustic variability in developing voices and the subtle distinction between pathological stuttering and typical developmental disfluencies. We introduce Paediatric-HGNN, a framework using a Context-aware Part-whole Interaction Network (CaPIN) tailored for paediatric data. Instead of conventional 1D signal modelling, our approach builds a heterogeneous graph capturing hierarchical relationships between lexical units (word nodes) and fine-grained acoustic segments (frame nodes). Trained on curated paediatric corpora (UCLASS and FluencyBank), Paediatric-HGNN achieves 82.4% weighted accuracy and a Typical Disfluency F1-score of 0.386. Modelling hierarchical lexical-acoustic interactions captures developmental "searching" behaviour, offering a more robust and interpretable tool for early clinical intervention.

Introduction: Socially assistive robots hold promise for enhancing therapeutic engagement in paediatric clinical settings. However, their successful implementation requires not only technical robustness but also context-sensitive, co-designed solutions. This paper presents Mobirobot, a socially assistive robot developed to support mobilisation in children recovering from trauma, fractures, or depressive disorders through personalised exercise programmes. Methods: An agile, human-centred development approach guided the iterative design of Mobirobot. Multidisciplinary clinical teams and end users were involved throughout the co-development process, which focused on early integration into real-world paediatric surgical and psychiatric settings. The robot, based on the NAO platform, features a simple setup, adaptable exercise routines with interactive guidance, motivational dialogue, and a graphical user interface (GUI) for monitoring and no-code system feedback. Results: Deployment in hospital environments enabled the identification of key design requirements and usability constraints. Stakeholder feedback led to refinements in interaction design, movement capabilities, and technical

We introduce Robust Bayesian Sequential Borrowing (RBSB), a framework for extrapolating evidence across adjacent subgroups in multi-population clinical programmes where studies are conducted in sequence and populations are ordered by clinical proximity. Conventional approaches weight all historical sources uniformly or exclude distant populations entirely, failing to reflect the natural gradient of similarity in such programmes. RBSB encodes the programme order through path-dependent borrowing via robust mixture priors that combine an informative component with a unit-information component to guard against prior-data conflict. Posterior weights, derived in closed form from marginal likelihood ratios, provide transparent dynamic attenuation when heterogeneity arises between sequential populations. The framework supports prospective evaluation of Bayesian Type I error, power, and extends naturally to assurance at both the study and programme level. Simulation studies demonstrate superior false-positive control relative to full pooling, while preserving substantial efficiency gains over standalone analyses. A case study of the START trial illustrates the approach across adult, adolesc

We examine three landmark clinical trials -- ECMO, CALGB~49907, and I-SPY~2 -- through a unified Bayesian framework connecting prior specification, sequential adaptation, and decision-theoretic optimisation. For ECMO, the posterior probability of treatment superiority is robust across the range of priors examined. For CALGB, predictive probability monitoring stopped enrolment at 633 instead of 1800 patients. For I-SPY~2, adaptive enrichment graduated nine of 23 arms to Phase~III. These case studies motivate a methodological contribution: exact backward induction for two-arm binary trials, where Beta-Binomial conjugacy yields closed-form transitions on the integer lattice of success counts with no quadrature. A Pólya-Gamma augmentation bridges this to covariate-adjusted logistic regression. Simulation reveals a fundamental tension: the optimal Bayesian design reduces expected sample sizes to 14--26 per arm (versus 42--100 for alternatives) but with substantially lower power. A calibrated variant embedding the declaration threshold in the terminal utility improves power while maintaining sample-size savings; varying the per-stage cost traces a power frontier for selecting the preferr

Single-arm studies in the early development phases of new treatments are not uncommon in the context of rare diseases or in paediatrics. If an assessment of efficacy is to be made at the end of such a study, the observed endpoints can be compared with reference values that can be derived from historical data. For a time-to-event endpoint, a statistical comparison with a reference curve can be made using the one-sample log-rank test. In order to ensure the interpretability of the results of this test, the role of the reference curve is crucial. This quantity is often estimated from a historical control group using a parametric procedure. Hence, it should be noted that it is subject to estimation uncertainty. However, this aspect is not taken into account in the one-sample log-rank test statistic. We analyse this estimation uncertainty for the common situation that the reference curve is estimated parametrically using the maximum likelihood method, and indicate how the variance estimation of the one-sample log-rank test can be adapted in order to take this variability into account. The resulting test procedures are illustrated using a data example and analysed in more detail using si

Electronic Patient Record (EPR) systems contain valuable clinical information, but much of it is trapped in unstructured text, limiting its use for research and decision-making. Large language models can extract such information but require substantial computational resources to run locally, and sending sensitive clinical data to cloud-based services, even when deidentified, raises significant patient privacy concerns. In this study, we develop a resource-efficient semi-automated annotation workflow using small language models (SLMs) to extract structured information from unstructured EPR data, focusing on paediatric histopathology reports. As a proof-of-concept, we apply the workflow to paediatric renal biopsy reports, a domain chosen for its constrained diagnostic scope and well-defined underlying biology. We develop the workflow iteratively with clinical oversight across three meetings, manually annotating 400 reports from a dataset of 2,111 at Great Ormond Street Hospital as a gold standard, while developing an automated information extraction approach using SLMs. We frame extraction as a Question-Answering task grounded by clinician-guided entity guidelines and few-shot exampl

Paediatric obstructive sleep apnoea (OSA) is clinically significant yet difficult to diagnose, as children poorly tolerate sensor-based polysomnography. Acoustic monitoring provides a non-invasive alternative for home-based OSA screening, but limited paediatric data hinders the development of robust deep learning approaches. This paper proposes a transfer learning framework that adapts acoustic models pretrained on adult sleep data to paediatric OSA detection, incorporating SpO2-based desaturation patterns to enhance model training. Using a large adult sleep dataset (157 nights) and a smaller paediatric dataset (15 nights), we systematically evaluate (i) single- versus multi-task learning, (ii) encoder freezing versus full fine-tuning, and (iii) the impact of delaying SpO2 labels to better align them with the acoustics and capture physiologically meaningful features. Results show that fine-tuning with SpO2 integration consistently improves paediatric OSA detection compared with baseline models without adaptation. These findings demonstrate the feasibility of transfer learning for home-based OSA screening in children and offer its potential clinical value for early diagnosis.

Sleep difficulties in children are heterogeneous in presentation, yet conventional assessment tools like the Children's Sleep Habits Questionnaire (CSHQ) reduce this complexity to a single cumulative score, obscuring distinct patterns of sleep disturbance that require different interventions. Latent Class Regression (LCR) models offer a principled approach to identify subgroups with shared sleep behaviour profiles whilst incorporating predictors of group membership, but Bayesian inference for these models has been hindered by computational challenges and the absence of variable selection methods. We propose a fully Bayesian framework for LCR that uses Pólya-Gamma data augmentation, enabling efficient sampling of regression coefficients. We extend this framework to include variable selection for both predictors and item responses: predictor variable selection via latent inclusion indicators and item selection through a partially collapsed approach. Through simulation studies, we show that the proposed methods yield accurate parameter estimates, resolve identifiability issues arising in full models and successfully identify informative predictors and items while excluding noise varia

Neuroblastoma is a paediatric extracranial solid cancer that arises from the developing sympathetic nervous system and is characterised by an abnormal distribution of cell types in tumours compared to healthy infant tissues. In this paper, we propose a new mathematical model of cell differentiation during sympathoadrenal development. By performing Bayesian inference of the model parameters using clinical data from patient samples, we show that the model successfully accounts for the observed differences in cell type heterogeneity among healthy adrenal tissues and four common types of neuroblastomas. Using a phenotypically structured model, we show that alterations in healthy differentiation dynamics are related to cell malignancy, and tumour volume growth. We use this model to analyse the evolution of malignant traits in a tumour. Our findings suggest that normal development dynamics make the embryonic sympathetic nervous system more robust to perturbations and accumulation of malignancies, and that the diversity of differentiation dynamics found in the neuroblastoma subtypes lead to unique risk profiles for neuroblastoma relapse after treatment.

Lung magnetic resonance imaging (MRI) with ultrashort echo-time (UTE) represents a recent breakthrough in lung structure imaging, providing image resolution and quality comparable to computed tomography (CT). Due to the absence of ionising radiation, MRI is often preferred over CT in paediatric diseases such as cystic fibrosis (CF), one of the most common genetic disorders in Caucasians. To assess structural lung damage in CF imaging, CT scoring systems provide valuable quantitative insights for disease diagnosis and progression. However, few quantitative scoring systems are available in structural lung MRI (e.g., UTE-MRI). To provide fast and accurate quantification in lung MRI, we investigated the feasibility of novel Artificial intelligence-assisted Pixel-level Lung (APL) scoring for CF. APL scoring consists of 5 stages, including 1) image loading, 2) AI lung segmentation, 3) lung-bounded slice sampling, 4) pixel-level annotation, and 5) quantification and reporting. The results shows that our APL scoring took 8.2 minutes per subject, which was more than twice as fast as the previous grid-level scoring. Additionally, our pixel-level scoring was statistically more accurate (p=0.0

Pneumonia remains one of the leading causes of death among children worldwide, underscoring a critical need for fast and accurate diagnostic tools. In this paper, we propose an interpretable deep learning model on Residual Networks (ResNets) for automatically diagnosing paediatric pneumonia on chest X-rays. We enhance interpretability through Bayesian Gradient-weighted Class Activation Mapping (BayesGrad-CAM), which quantifies uncertainty in visual explanations, and which offers spatial locations accountable for the decision-making process of the model. Our ResNet-50 model, trained on a large paediatric chest X-rays dataset, achieves high classification accuracy (95.94%), AUC-ROC (98.91%), and Cohen's Kappa (0.913), accompanied by clinically meaningful visual explanations. Our findings demonstrate that high performance and interpretability are not only achievable but critical for clinical AI deployment.

Paediatric Acute Myeloid Leukemia is a complex adaptive ecosystem with high morbidity. Current trajectory inference algorithms struggle to predict causal dynamics in AML progression, including relapse and recurrence risk. We propose a symbolic AI and deep learning framework grounded in complexity science, integrating Recurrent Neural Networks, Transformers, and Algorithmic Information Dynamics to model longitudinal single cell transcriptomics and infer complex state transitions in paediatric AML. We identify key plasticity markers as predictive signatures regulating developmental trajectories. These were derived by integrating deep learning with complex systems based network perturbation analysis and dynamical systems theory to infer high dimensional state space attractors steering AML evolution. Findings reveal dysregulated epigenetic and developmental patterning, with AML cells in maladaptive, reprogrammable plastic states, i.e., developmental arrest blocking terminal differentiation. Predictions forecast neurodevelopmental and morphogenetic signatures guiding AML cell fate bifurcations, suggesting ectoderm mesoderm crosstalk during disrupted differentiation. Neuroplasticity and

Brain tumour MRI typically requires both pre- and post-contrast imaging, but gadolinium is not always desirable (frequent follow-up, renal impairment, allergy, paediatric patients). We developed and validated a deep learning model to predict tumour contrast enhancement from non-contrast MRI alone. We assembled 11,089 brain MRI studies (2006-2024) from 10 datasets across four countries and three continents, spanning adult and paediatric populations with glioma, meningioma, metastases, and post-resection appearances. Three architectures were trained to detect and segment enhancing tumour from T1w, T2w and FLAIR alone. Performance was assessed in a 1,109-study held-out test set (primary endpoint: patient-level enhancement detection; secondary: voxel-level Dice). Eleven expert radiologists attempted the same task on a 564-case subset (100 cases each), blinded to history, prior imaging, and referral. The best model, nnU-Net, achieved 83.0% balanced accuracy (95% CI 79.1-87.2; sensitivity 91.5%, specificity 74.4%) for detection, with R2 = 0.859 for enhancement volume. Of enhancing cases, 76.8% reached Dice >= 0.3, 67.5% >= 0.5, and 50.2% >= 0.7. Under blinded conditions, radiolo

Magnetic resonance imaging (MRI) is critical for neurodevelopmental research, however access to high-field (HF) systems in low- and middle-income countries is severely hindered by their cost. Ultra-low-field (ULF) systems mitigate such issues of access inequality, however their diminished signal-to-noise ratio limits their applicability for research and clinical use. Deep-learning approaches can enhance the quality of scans acquired at lower field strengths at no additional cost. For example, Convolutional neural networks (CNNs) fused with transformer modules have demonstrated a remarkable ability to capture both local information and long-range context. Unfortunately, the quadratic complexity of transformers leads to an undesirable trade-off between long-range sensitivity and local precision. We propose a hybrid CNN and state-space model (SSM) architecture featuring a novel 3D to 1D serialisation (GAMBAS), which learns long-range context without sacrificing spatial precision. We exhibit improved performance compared to other state-of-the-art medical image-to-image translation models.

Paediatric kidney disease varies widely in its presentation and progression, which calls for continuous monitoring of renal function. Using electronic health records collected between 2019 and 2025 at Great Ormond Street Hospital, a leading UK paediatric hospital, we explored a temporal modelling approach that integrates longitudinal laboratory sequences with demographic information. A recurrent neural model trained on these data was used to predict whether a child would record an abnormal serum creatinine value within the following thirty days. Framed as a pilot study, this work provides an initial demonstration that simple temporal representations can capture useful patterns in routine paediatric data and lays the groundwork for future multimodal extensions using additional clinical signals and more detailed renal outcomes.

Background Brain tumours are the most common solid malignancies in children, encompassing diverse histological, molecular subtypes and imaging features and outcomes. Paediatric brain tumours (PBTs), including high- and low-grade gliomas (HGG, LGG), medulloblastomas (MB), ependymomas, and rarer forms, pose diagnostic and therapeutic challenges. Deep learning (DL)-based segmentation offers promising tools for tumour delineation, yet its performance across heterogeneous PBT subtypes and MRI protocols remains uncertain. Methods A retrospective single-centre cohort of 174 paediatric patients with HGG, LGG, medulloblastomas (MB), ependymomas, and other rarer subtypes was used. MRI sequences included T1, T1 post-contrast (T1-C), T2, and FLAIR. Manual annotations were provided for four tumour subregions: whole tumour (WT), T2-hyperintensity (T2H), enhancing tumour (ET), and cystic component (CC). A 3D nnU-Net model was trained and tested (121/53 split), with segmentation performance assessed using the Dice similarity coefficient (DSC) and compared against intra- and inter-rater variability. Results The model achieved robust performance for WT and T2H (mean DSC: 0.85), comparable to human a

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses.