AI Medical Compendium Topic

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Interpreting IGF-1 in children treated with recombinant growth hormone: challenges during early puberty.

Frontiers in endocrinology
OBJECTIVE: It can be challenging to determine the correct dosage of recombinant growth hormone (GH) in children with GH deficiency, leading to highly variable treatment responses. Insulin-like growth factor-1 (IGF-1) is a tool for monitoring GH treat...

TractGraphFormer: Anatomically informed hybrid graph CNN-transformer network for interpretable sex and age prediction from diffusion MRI tractography.

Medical image analysis
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network desi...

Machine learning demonstrates clinical utility in distinguishing retinoblastoma from pseudo retinoblastoma with RetCam images.

Ophthalmic genetics
BACKGROUND: Retinoblastoma is diagnosed and treated without biopsy based solely on appearance (with the indirect ophthalmoscope and imaging). More than 20 benign ophthalmic disorders resemble retinoblastoma and errors in diagnosis continue to be made...

Predictors of glycaemic improvement in children and young adults with type 1 diabetes and very elevated HbA1c using the MiniMed 780G system.

Diabetes, obesity & metabolism
AIMS: This study aimed to identify key factors with the greatest influence on glycaemic outcomes in young individuals with type 1 diabetes (T1D) and very elevated glycaemia after 3 months of automated insulin delivery (AID).

A multi-modal dental dataset for semi-supervised deep learning image segmentation.

Scientific data
In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing...

Automated segmentation of child-clinician speech in naturalistic clinical contexts.

Research in developmental disabilities
BACKGROUND: Computational approaches hold significant promise for enhancing diagnosis and therapy in child and adolescent clinical practice. Clinical procedures heavily depend n vocal exchanges and interpersonal dynamics conveyed through speech. Rese...

Diagnosing Epilepsy with Normal Interictal EEG Using Dynamic Network Models.

Annals of neurology
OBJECTIVE: Whereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of routine EEGs show interictal epileptiform discharges (IEDs) and overall mi...

Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques.

Neuroinformatics
The problem at hand is the significant global health challenge posed by children's diseases, where timely and accurate diagnosis is crucial for effective treatment and management. Conventional diagnosis techniques are typical, use tedious processes a...

Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study.

Scientific reports
We aimed to analyze the cervical sagittal alignment change following the growing rod treatment in early-onset scoliosis (EOS) and identify the risk factors of sagittal cervical imbalance after growing-rod surgery of machine learning. EOS patients fro...

Assessment of using transfer learning with different classifiers in hypodontia diagnosis.

BMC oral health
BACKGROUND: Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision sup...