AIMC Topic: Child, Preschool

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Predicting 30-day mortality in hemophagocytic lymphohistiocytosis: clinical features, biochemical parameters, and machine learning insights.

Annals of hematology
This study aims to evaluate the clinical characteristics and biochemical parameters of hemophagocytic lymphohistiocytosis (HLH) patients to predict 30-day mortality. Parameters analyzed include lymphocyte count (L), platelet count (PLT), total protei...

Machine learning for early diagnosis of Kawasaki disease in acute febrile children: retrospective cross-sectional study in China.

Scientific reports
Early diagnosis of Kawasaki disease (KD) allows timely treatment to be initiated, thereby preventing coronary artery aneurysms in children. However, it is challenging due to the subjective nature of the diagnostic criteria. This study aims to develop...

Deep Learning-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.

Academic radiology
RATIONALE AND OBJECTIVES: This study assesses the image quality of temporal bone ultra-high-resolution (UHR) Computed tomography (CT) scans in adults and children using hybrid iterative reconstruction (HIR) and a novel, vendor-specific deep learning-...

AI-MET: A deep learning-based clinical decision support system for distinguishing multisystem inflammatory syndrome in children from endemic typhus.

Computers in biology and medicine
The COVID-19 pandemic brought several diagnostic challenges, including the post-infectious sequelae multisystem inflammatory syndrome in children (MIS-C). Some of the clinical features of this syndrome can be found in other pathologies such as Kawasa...

Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
OBJECTIVES: Pediatric elbow fractures are a common injury among children. Recent advancements in artificial intelligence (AI), particularly deep learning (DL), have shown promise in diagnosing these fractures. This study systematically evaluated the ...

AI-facilitated home monitoring for cystic fibrosis exacerbations across pediatric and adult populations.

Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
BACKGROUND: AI-aided home stethoscopes offer the opportunity of continuous remote monitoring of cystic fibrosis (CF) patients, reducing the need for clinic visits.

A multimodal machine learning algorithm improved diagnostic accuracy for otitis media in a school aged Aboriginal population.

Journal of biomedical informatics
OBJECTIVE: Otitis Media (OM) - ear infection - can lead to hearing loss and associated developmental delay. There are several subgroups of OM which can be difficult to diagnose accurately, even for experienced clinicians. AI and machine learning algo...

An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.

Scientific reports
Auscultation is a method that involves listening to sounds from the patient's body, mainly using a stethoscope, to diagnose diseases. The stethoscope allows for non-invasive, real-time diagnosis, and it is ideal for diagnosing respiratory diseases an...

Machine Learning-Driven Identification of Molecular Subgroups in Medulloblastoma via Gene Expression Profiling.

Clinical oncology (Royal College of Radiologists (Great Britain))
AIMS: Medulloblastoma (MB) is the most prevalent malignant brain tumour in children, characterised by substantial molecular heterogeneity across its subgroups. Accurate classification is pivotal for personalised treatment strategies and prognostic as...