Pediatric Radiology最新文献

Background: While single-energy hand computed tomography angiography (CTA) often yields suboptimal visualization of distal vessels, dual-energy computed tomography (CT) with low-keV virtual monoenergetic image (VMI) reconstruction enhances small-vessel conspicuity.

Objective: To evaluate the value of dual-energy CT VMIs in pediatric hand CTA.

Materials and methods: This retrospective study included 49 pediatric patients. Seven image series per patient were generated from dual-energy data: an M_0.5 image (50% 70 kVp+50% tin-filtered 150 kVp), a 70-kVp image, and five VMIs at 40-80 keV (10-keV increments). Objective metrics (attenuation, vessel noise, signal-to-noise ratio, contrast-to-noise ratio) and subjective scores were assessed for five vessels: the radial artery, the ulnar artery, the common palmar digital artery, and the proximal and distal parts of the proper palmar digital artery. Subjective image quality was independently evaluated by two radiologists using a 4-point Likert scale.

Results: The 40-keV VMIs provided the highest vascular attenuation across all vessels, albeit with the highest noise. Subjective scores for the radial, ulnar, and common palmar digital arteries showed no significant differences among the 40-keV, 50-keV, and 70-kVp series. However, for the small distal proper palmar digital arteries and total image quality, the 40-keV series was rated superior to the other series. No significant differences in image quality existed between the 70-kVp and 50-keV images.

Conclusion: For pediatric hand CTA, 40-keV VMIs provide optimal vascular conspicuity for small distal vessels, yielding the highest diagnostic confidence and total image quality score, and this benefit outweighs the associated increase in vessel noise.

Background: Extralobar pulmonary sequestration can undergo torsion within the pleural cavity, which represents a rare and the most severe complication in childhood. To date, no data have been published on the use of contrast-enhanced ultrasound (CEUS) in extralobar pulmonary sequestration with torsion.

Objective: The objective of this study was to retrospectively analyse the sonographic features of pediatric extralobar pulmonary sequestration with torsion on gray-scale ultrasound (US) and CEUS.

Materials and methods: A retrospective observational study was conducted in eight children with clinically and histologically confirmed extralobar pulmonary sequestration with torsion between January 2020 and September 2024. Gray-scale US findings were available for all eight cases, and CEUS features were obtained and reviewed in detail in four of these patients.

Results: All lesions were solitary, with a right-to-left ratio of 5:3. On gray-scale US, torsional extralobar pulmonary sequestration demonstrated a regular morphology and well-defined margins in all cases. Heterogeneous echotexture was observed in five cases, including cystic structures in two cases and linear branching structures in two cases. On CEUS, absence of enhancement in the early pulmonary arterial phase was identified in all four patients (100%). In the delayed bronchial arterial phase, stem-shaped enhancement confined to the base of the mass was observed in three patients (75%), including one case in which a feeding artery was visualised. Peripheral ring-shaped enhancement during the bronchial arterial phase was present in all four cases (100%). Other associated pulmonary findings included pleural effusion (8/8, 100%) and consolidation (4/8, 50%).

Conclusion: On gray-scale ultrasound, extralobar pulmonary sequestration with torsion typically appears as a well-defined mass with a regular shape in the lower thoracic cavity. On CEUS, stem-shaped enhancement at the base of the mass during the delayed bronchial arterial phase may represent a useful imaging feature for predicting extralobar pulmonary sequestration with torsion.

Data mining is the systematic process of extracting useful knowledge from large multimodal datasets and is increasingly enabled by artificial intelligence (AI) methods. Pediatric radiology is a natural field for data mining because multimodal data sources, including images, reports, metadata, and electronic health records, together capture rich information on anatomy, disease, treatment, and outcomes. In the current era, the boundaries between data mining and AI are increasingly blurred. AI assists in key steps of the mining workflow through automated labeling, information extraction, and representation learning, while data mining provides the high-quality curated datasets that underpin model performance, generalizability, and safety. This review, therefore, examines both domains together, emphasizing their interdependence in the pediatric context. We describe core concepts and workflows of data mining in pediatric radiology, including data collection, linkage, annotation, analysis, validation, and governance, and outline how modern AI tools such as deep learning, large language models, multimodal fusion, and federated learning support advanced pattern discovery across limited and heterogeneous pediatric datasets. We summarize current and emerging clinical applications across diagnosis, prognosis, radiation dose monitoring, operational analytics, reporting safety nets, and continual learning. We then discuss current challenges related to data quality and standardization, ethics, regulation, workflow integration, resource disparities, sustainability, and explainability. Finally, we highlight future perspectives, including synthetic data generation, foundation models, structured reporting, and pediatric-focused ethical frameworks that aim to enable safe, transparent, and equitable integration of AI-driven data mining to improve outcomes in children.

Pediatric imaging presents distinct and urgent sustainability challenges, in part driven by its unique subspecialty demands: safeguarding the lifetime radiation risks of children, providing accurate diagnoses during their dynamic periods of growth, and ensuring family-centered care. These unique challenges impose additional strains on our ecosystem. To help alleviate this added burden, we propose a three-pillar model of sustainability specific to pediatric imaging, encompassing environmental, economic, and social factors. In particular, we address the sustainability challenges central to pediatric radiology by introducing AI not only as a tool for diagnostic accuracy, but also as an engine for sustainable practice. In this review, we move beyond the generic discussions of "green" radiology by illustrating how AI can be deployed to confront specific challenges across all three pillars of sustainability. Our review is centered around nine concrete, clinically grounded AI solutions, with three examples dedicated to each pillar. When strategically applied, these AI solutions have the potential to optimize energy efficiency, decrease consumables, extend equipment lifecycles, streamline operations, increase revenue, enhance transparency, improve pediatric care, promote equity, and empower patients and families. We also address other critical considerations in this sustainability domain, including AI's own carbon footprint and the need for pediatric-specific validation. Collectively, AI's extensive capabilities can drive our pediatric imaging towards diagnostic excellence, while optimizing environmental health, operational efficiency, and social equity.

This narrative review maps the current landscape of artificial intelligence (AI) in paediatric and fetal neuroradiology, critically evaluating current practice, barriers to clinical adoption, and future potential. We searched for peer-reviewed studies from the last decade, focusing on image segmentation, lesion detection, classification, prognostication, and clinical decision support in paediatric brain imaging. Particular consideration was given to unique paediatric factors such as brain development and data scarcity. AI techniques, notably deep learning, have demonstrated success in automated brain tumour segmentation, detection of epileptogenic lesions, and radiomics-based classifiers predicting tumour histology and molecular subtypes. Despite these advancements, clinical adoption remains limited. Key barriers identified include high implementation costs, limited large-scale diverse paediatric datasets, and concerns regarding safety, bias, and regulatory approval. Addressing these issues through data-sharing initiatives, federated learning, paediatric-specific validation, and revised ethical and regulatory frameworks is crucial. Ongoing multi-institutional collaborations can facilitate AI's integration into paediatric neuroradiology, complementing radiologists and improving paediatric care.

Background: The integration of deep learning in medical imaging has reached proficiency levels akin to expert clinicians, particularly in tasks requiring precise image categorization.

Objective: This study developed KD-SqueezeNet, a lightweight deep learning model, to classify neonatal lung diseases via chest radiographs, aiming to enhance diagnostic accuracy and efficiency.

Materials and methods: Retrospective analysis included 2,089 neonates with clinical and imaging records. Chest radiographs were categorized into five groups: bronchopulmonary dysplasia (group 1, n=205), pneumonia (group 2, n=505), pneumothorax (group 3, n=201), respiratory distress syndrome (group 4, n=629), and normal (group 5, n=549). Data were divided into training, testing, and validation sets with an 8:1:1 ratio. Performance metrics included accuracy (Ac), precision (Pr), recall (Rc), F1 score (F1), parameter count, and area under the receiver operating characteristic curve (AUROC).

Results: KD-SqueezeNet, an interpretable model integrating knowledge distillation, outperformed EfficientNet, GhostNet, InceptionNet, RegNet, and Vision Transformer. For binary classification (healthy/diseased), it achieved Ac=0.93, Pr=0.93, Rc=0.93, F1=0.93, and AUROC=0.97 with only 723,522 parameters. In four-class classification (group 1/group 2/group 3/group 4), it attained Ac=0.86, Pr=0.86, Rc=0.86, and F1=0.86 (724,548 parameters), with class-specific AUROCs: group 1 (0.97), group 2 (0.94), group 3 (0.96), group 4 (0.97).

Conclusion: KD-SqueezeNet not only excels in accuracy and stability but also demonstrates efficient utilization of computational resources, making it suitable for rapid diagnosis and deployment in practical applications. It holds significant clinical value in terms of saving time and server space, providing auxiliary diagnostics, supporting clinical decision-making, and improving patient outcomes in large-scale screening contexts.

Background: Pediatric tuberculosis diagnosis relies heavily on imaging, yet access, equipment standards, and dose monitoring differ widely across health systems. Evidence describing how these contextual factors influence imaging use and radiation exposure in children remains scarce.

Objective: To describe pediatric tuberculosis imaging practices and estimated radiation doses across two distinct resource settings, Spain (hospital-based, high-resource) and Mozambique (primary care-based, low-resource), to inform strategies for safe, equitable, and context-appropriate imaging.

Methods and materials: A descriptive mixed-methods study combined retrospective data of children (<16 years) diagnosed with tuberculosis (Spain 2015-2021; Mozambique 2018-2021) with complementary surveys of imaging providers. In Spain, chest X-ray and computed tomography parameters were extracted from digital imaging and communications in medicine files to estimate organ-specific doses using the National Cancer Institute dosimetry systems for radiography and computed tomography. In Mozambique, dose estimates were based on standardized pediatric protocols and site survey parameters due to limited digital data. Surveys captured information on imaging access, guideline use, and professional training.

Results: Imaging data were available for 84 Spanish and 83 Mozambican children. In Spain, children underwent multiple chest X-rays (mean four per child) and computed tomographies (mean three per child), resulting in cumulative lung doses up to ~20 mGy cm², remaining below diagnostic reference levels. In Mozambique, most children had one or two chest X-rays, with cumulative lung doses <0.05 mGy cm². Survey findings indicated structured dose optimization and quality assurance practices in Spain, versus limited equipment and predominantly non-physician interpretation in Mozambique.

Conclusion: Context-appropriate improvements in pediatric imaging such as strengthened infrastructure, training, dose monitoring, and quality assurance are essential to ensure safe exposure and equitable, reliable tuberculosis diagnosis for children.

Background: White matter injury is a leading cause of neurodevelopmental impairment in premature infants. Timely initiation of novel therapies in clinical development will require early identification of clinically significant white matter injury. Head ultrasound is commonly obtained at 7 days and 30 days of life (DOL) to screen for brain injuries in premature infants.

Objective: To determine if white matter injury severity on head ultrasound at 7 DOL and 30 DOL is associated with white matter injury severity on term-equivalent age MRI and with neurological outcomes through age 2 years.

Materials and methods: We identified subjects via a search of the electronic health record for preterm infants born at ≤32 weeks gestational age (GA) with evidence of white matter injury in neuroimaging reports. Head ultrasounds at 7 days and 30 days and term-equivalent age MRIs were scored using established scoring systems by three expert readers, with final scoring established by consensus. We used ordinal logistic regression to determine the association between white matter severity on ultrasound and MRI. Multivariable models were adjusted for GA at birth and severity of intraventricular hemorrhage. Neurological outcomes (cerebral palsy, epilepsy, and neurosensory impairment) were determined by medical records review with a median corrected age at follow-up of 23.0 months.

Results: Fifty infants with a median GA at birth of 27.1 weeks were included in our retrospective cohort. White matter injury severity on 7-DOL (odds ratio 1.8, 95% CI 1.3-2.6) and 30-DOL (odds ratio 1.5, 95% CI 1.2-2.0) ultrasound was independently associated with severity on MRI. Higher injury severity on 7-DOL ultrasound was associated with cerebral palsy (odds ratio 2.4, 95% CI 1.3-4.3), while higher injury severity on 30-DOL ultrasound was associated with both cerebral palsy (odds ratio 1.7, 95% CI 1.2-2.5) and neurosensory impairment (odds ratio 1.7, 95% CI 1.2-2.4).

Conclusion: Preterm infants with white matter injury on 7-DOL or 30-DOL head ultrasound are at elevated risk for white matter injury on term-equivalent age brain MRI and for future neurological impairment.