OBJECTIVE: To evaluate the clinical utility of improved machine learning models in predicting poor prognosis following endovascular intervention for intracranial aneurysms and to develop a corresponding visualization system.
OBJECTIVE: To develop a machine learning-based model for predicting the clinical efficacy of acupuncture intervention in patients with upper limb dysfunction following ischemic stroke, and to assess its potential role in guiding clinical practice.
OBJECTIVE: This study aims to evaluate key factors influencing the short-term and long-term prognosis of stroke patients, with a particular focus on variables such as body weight, hemoglobin, electrolytes, kidney function, organ function scores, and ...
BACKGROUND: Disorders of consciousness (DoC) in non-traumatic ICU-patients are often treated with amantadine, although evidence supporting its efficacy is limited.
OBJECTIVE: To develop a machine learning-based clinical and/or radiomics model for predicting the primary site of brain metastases using multiparametric magnetic resonance imaging (MRI).
OBJECTIVE: Neurological deterioration after mild traumatic brain injury (TBI) has been recognized as a poor prognostic factor. Early detection of neurological deterioration would allow appropriate monitoring and timely therapeutic interventions to im...
BACKGROUND: Approximately 103 million people across the globe suffer from symptomatic lumbar spinal stenosis, impacting their health and quality of life. The unilateral biportal endoscopic technique is effective for treating single-segment degenerati...
BACKGROUND: The safe timing window for surgery during the acute phase of inflammation due to traumatic brain injury (TBI) has not been studied extensively. We aimed to elucidate the relationship between the timing of surgery and changes in perioperat...
OBJECTIVE: We aimed to investigate the relationship between volumetric measurements of specific brain regions which were measured with artificial intelligence (AI) and various neuropsychological tests in patients with clinically isolated syndrome.
BACKGROUND AND AIM: Neurodegenerative disorders (e.g., Alzheimer's, Parkinson's) lead to neuronal loss; neurocognitive disorders (e.g., delirium, dementia) show cognitive decline. Early detection is crucial for effective management. Machine learning ...