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Deep learning approach for tooth numbering and restoration detection on pediatric periapical radiographs in mixed dentition.

Clinical oral investigations
OBJECTIVES: Accurate tooth numbering and restoration detection on periapical radiographs in mixed dentition are critical to the treatment planning process. They also improve the speed and accuracy of treatment processes by automating the early diagno...

Segmenting beyond the imaging data: creation of anatomically valid edentulous mandibular geometries for surgical planning using artificial intelligence.

Clinical oral investigations
BACKGROUND AND OBJECTIVES: Mandibular reconstruction following continuity resection due to tumor ablation or osteonecrosis remains a significant challenge in maxillofacial surgery. Virtual surgical planning (VSP) relies on accurate segmentation of th...

Distinct electroencephalogram microstate in patients with methamphetamine use disorder and obsessive-compulsive disorder.

Journal of affective disorders
BACKGROUND: Electroencephalogram (EEG) microstates reflect momentary localized brain activity and may indicate spontaneous fluctuations within large-scale neural networks. Methamphetamine use disorder (MUD) and obsessive-compulsive disorder (OCD) exh...

Multifactorial Biomarkers for "Talk and Deteriorate" after Head Trauma Identified Using Machine Learning.

Neurologia medico-chirurgica
Talk and Deteriorate refers to a clinical course where a patient is able to speak immediately after a traumatic brain injury but subsequently deteriorates in consciousness. Talk and Deteriorate outcomes are poor, and reliable prediction may help impr...

Linarin protects against asthma-induced airway epithelial ferroptosis and inflammation via ALDH2 regulation.

Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND: Asthma, a complex disease characterized by airway epithelial dysfunction and chronic inflammation, poses ongoing therapeutic challenges.

Comparative analysis of robotic assisted vs. traditional spinal angiography in a large single-center experience.

Journal of the neurological sciences
BACKGROUND: Spinal angiography (SA) remains the gold standard for evaluating spinal cord vasculature, but traditional approaches expose operators and patients to significant ionizing radiation. Robotic-assisted platforms offer potential advantages th...

Using machine learning models to predict vaccine hesitancy: a showcase of COVID-19 vaccine hesitancy in rural populations during the pandemic.

Vaccine
Understanding vaccine hesitancy is a critical public health challenge, yet traditional statistical methods often fail to capture the complex drivers behind it. This study uses COVID-19 vaccine hesitancy in a rural population as a case study to demons...

Machine Learning Models to Predict Withdrawal of Life-Sustaining Therapy in Patients With Severe Traumatic Brain Injury.

Neurology
BACKGROUND AND OBJECTIVES: Over half of all deaths after traumatic brain injury (TBI) follow the decision to withdraw life-sustaining therapy (WLST). Despite recent improvements in TBI mortality, rates of WLST have remained unchanged, potentially ref...

Development of a Novel Hydroxylamine-Based Stable Isotope Labeling Reagent for Profiling Aldehyde Metabolic Biomarkers in Diabetes Using LC-MS/MS and Machine Learning.

Analytical chemistry
Aldehyde compounds are significantly associated with diabetes mellitus. The metabolic profile of aldehydes can enhance understanding of the mechanisms underlying development of diabetes. This study employed a pair of stable isotope labeling (SIL) rea...

Sustainable deep learning-based breast lesion segmentation: impact of breast region segmentation on performance.

BMC medical imaging
PURPOSE: Segmentation of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is critical for effective diagnosis. This study investigates the impact of breast region segmentation (BRS) on the performance of deep learning-...