INTRODUCTION: We developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre-implant brain fMRI data from the candidate.
INTRODUCTION: Post-stroke depression is one of the important complications of stroke and affects patients' quality of life. Early identification of post-stroke depression is crucial for its timely prevention. The accuracy of machine learning as a pre...
OBJECTIVE: To investigate the performance of machine learning (ML) methods based on resting-state functional magnetic resonance imaging (rs-fMRI) parameters in distinguishing children with intermittent exotropia (IXT) from healthy controls (HCs).
PURPOSE: Malnutrition remains a critical public health issue in low-income countries, significantly hindering economic development and contributing to over 50% of infant deaths. Under nutrition weakens immune systems, increasing susceptibility to com...
OBJECTIVES: The current study aimed to investigate the impacts of 8-week high-intensity interval training (HIIT) and Ritalin (RIT), alone and in combination, on cognitive functions and hippocampal oxidative parameters following chronic ethanol consum...
PROBLEM: Brain tumors are among the most prevalent and lethal diseases. Early diagnosis and precise treatment are crucial. However, the manual classification of brain tumors is a laborious and complex task.
BACKGROUND: The relentless integration of Artificial Intelligence (AI) into neurosurgery necessitates a meticulous exploration of the associated ethical concerns. This systematic review focuses on synthesizing empirical studies, reviews, and opinion ...
PURPOSE: This study presents a novel application of deep learning to enhance the accuracy of left-right orientation identification in anatomical brain MRI scans. Left-right orientation misidentification in brain MRIs presents significant challenges d...
INTRODUCTION: Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effective tools for early identification or prediction of this risk. As machine learning (ML) is increas...
PURPOSE: The neurobiological heterogeneity present in schizophrenia remains poorly understood. This likely contributes to the limited success of existing treatments and the observed variability in treatment responses. Our objective was to employ magn...