AI Medical Compendium Topic

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Child, Preschool

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Algorithmic and sensor-based research on Chinese children's and adolescents' screen use behavior and light environment.

Frontiers in public health
BACKGROUND: Myopia poses a global health concern and is influenced by both genetic and environmental factors. The incidence of myopia tends to increase during infectious outbreaks, such as the COVID-19 pandemic. This study examined the screen-time be...

Accelerating computer vision-based human identification through the integration of deep learning-based age estimation from 2 to 89 years.

Scientific reports
Computer Vision (CV)-based human identification using orthopantomograms (OPGs) has the potential to identify unknown deceased individuals by comparing postmortem OPGs with a comprehensive antemortem CV database. However, the growing size of the CV da...

A machine-learning exploration of the exposome from preconception in early childhood atopic eczema, rhinitis and wheeze development.

Environmental research
BACKGROUND: Most previous research on the environmental epidemiology of childhood atopic eczema, rhinitis and wheeze is limited in the scope of risk factors studied. Our study adopted a machine learning approach to explore the role of the exposome st...

Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy.

Journal of applied clinical medical physics
PURPOSE: Radiation therapy (RT) of pediatric brain cancer is known to be associated with long-term neurocognitive deficits. Although target and organs-at-risk (OARs) are contoured as part of treatment planning, other structures linked to cognitive fu...

Automated Detection of Pediatric Foreign Body Aspiration from Chest X-rays Using Machine Learning.

The Laryngoscope
OBJECTIVE/HYPOTHESIS: Standard chest radiographs are a poor diagnostic tool for pediatric foreign body aspiration. Machine learning may improve upon the diagnostic capabilities of chest radiographs. The objective is to develop a machine learning algo...

Classification of self-limited epilepsy with centrotemporal spikes by classical machine learning and deep learning based on electroencephalogram data.

Brain research
Electroencephalogram (EEG) has been widely utilized as a valuable assessment tool for diagnosing epilepsy in hospital settings. However, clinical diagnosis of patients with self-limited epilepsy with centrotemporal spikes (SeLECTS) is challenging due...

Machine learning-based prediction of vitamin D deficiency: NHANES 2001-2018.

Frontiers in endocrinology
BACKGROUND: Vitamin D deficiency is strongly associated with the development of several diseases. In the current context of a global pandemic of vitamin D deficiency, it is critical to identify people at high risk of vitamin D deficiency. There are n...

Machine learning-based prediction of the outcomes of cochlear implantation in patients with inner ear malformation.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
OBJECTIVE: The objectives of this study are twofold: first, to visualize the structure of malformed cochleae through image reconstruction; and second, to develop a predictive model for postoperative outcomes of cochlear implantation (CI) in patients ...

Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan.

Fluids and barriers of the CNS
BACKGROUND: Peri-sinus structures such as arachnoid granulations (AG) and the parasagittal dural (PSD) space have gained much recent attention as sites of cerebral spinal fluid (CSF) egress and neuroimmune surveillance. Neurofluid circulation dysfunc...