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Development and validation of machine-learning models of diet management for hyperphenylalaninemia: a multicenter retrospective study.

BMC medicine
BACKGROUND: Assessing dietary phenylalanine (Phe) tolerance is crucial for managing hyperphenylalaninemia (HPA) in children. However, traditionally, adjusting the diet requires significant time from clinicians and parents. This study aims to investig...

A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study.

BMC medicine
BACKGROUND: The pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment...

Proteomics profiling and machine learning in nusinersen-treated patients with spinal muscular atrophy.

Cellular and molecular life sciences : CMLS
AIM: The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need for reliable prognostic biomarkers to classify patients according to disease severity. We aim to identi...

A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis.

IEEE journal of translational engineering in health and medicine
Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a child into a correct voiding routing while existing pre-voiding systems suffer from several practical ...

Siamese based deep neural network for ADHD detection using EEG signal.

Computers in biology and medicine
BACKGROUND: Detecting Attention-Deficit/Hyperactivity Disorder (ADHD) in children is crucial for timely intervention and personalized treatment.

MR Cranial Bone Imaging: Evaluation of Both Motion-Corrected and Automated Deep Learning Pseudo-CT Estimated MR Images.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: CT imaging exposes patients to ionizing radiation. MR imaging is radiation free but previously has not been able to produce diagnostic-quality images of bone on a timeline suitable for clinical use. We developed automated moti...

Etiological stratification and prognostic assessment of haemophagocytic lymphohistiocytosis by machine learning on onco-mNGS data and clinical data.

Frontiers in immunology
INTRODUCTION: Hemophagocytic lymphohistiocytosis (HLH) is a rare, complicated and life threatening hyperinflammatory syndrome that maybe triggered by various infectious agents, malignancies and rheumatologic disorders. Early diagnosis and identificat...

Generative AI in Pediatric Gastroenterology.

Current gastroenterology reports
PURPOSE OF REVIEW: The integration of digital technology into medical practice is often thrust upon clinicians, with standards and routines developed long after initiation. Clinicians should endeavor towards a basic understanding even of emerging tec...

A machine learning model for early diagnosis of type 1 Gaucher disease using real-life data.

Journal of clinical epidemiology
OBJECTIVE: The diagnosis of Gaucher disease (GD) presents a major challenge due to the high variability and low specificity of its clinical characteristics, along with limited physician awareness of the disease's early symptoms. Early and accurate di...

Plasma cell-free RNA signatures of inflammatory syndromes in children.

Proceedings of the National Academy of Sciences of the United States of America
Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circul...