AIMC Topic: Female

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Computerized detection of leukocytes in microscopic leukorrhea images.

Medical physics
PURPOSE: Detection of leukocytes is critical for the routine leukorrhea exam, which is widely used in gynecological examinations. An elevated vaginal leukocyte count in women with bacterial vaginosis is a strong predictor of vaginal or cervical infec...

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Scientific reports
Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electr...

The Economic Burden of ACPA-Positive Status Among Patients with Rheumatoid Arthritis.

Journal of managed care & specialty pharmacy
BACKGROUND: Anticitrullinated protein antibodies (ACPAs) are serological biomarkers associated with early, rapidly progressing rheumatoid arthritis (RA), including more severe disease and joint damage. ACPA testing has become a routine tool for RA di...

Convolutional neural network-based encoding and decoding of visual object recognition in space and time.

NeuroImage
Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of vi...

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning.

International journal of neural systems
In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The...

Feature selection before EEG classification supports the diagnosis of Alzheimer's disease.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered to find relevant/non-redunda...

Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma.

Scientific reports
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in ...

Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children.

Scientific reports
The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laborat...

Can Lokomat therapy with children and adolescents be improved? An adaptive clinical pilot trial comparing Guidance force, Path control, and FreeD.

Journal of neuroengineering and rehabilitation
BACKGROUND: Robot-assisted gait therapy is increasingly being used in pediatric neurorehabilitation to complement conventional physical therapy. The robotic device applied in this study, the Lokomat (Hocoma AG, Switzerland), uses a position control m...

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

Medical & biological engineering & computing
Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning fra...