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Shear wave trajectory detection in ultra-fast M-mode images for liver fibrosis assessment: A deep learning-based line detection approach.

Ultrasonics
Stiffness measurement using shear wave propagation velocity has been the most common non-invasive method for liver fibrosis assessment. The velocity is captured through a trace recorded by transient ultrasonographic elastography, with the slope indic...

Using machine learning algorithms to detect fear of falling in people with multiple sclerosis in standardized gait analysis.

Multiple sclerosis and related disorders
INTRODUCTION: Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. The progressive impairment of gait is one of the most important pathognomic symptoms which are associated with falls and fear of fall...

Establishing central sensitization inventory cut-off values in Dutch-speaking patients with chronic low back pain by unsupervised machine learning.

Computers in biology and medicine
BACKGROUND: Human Assumed Central Sensitization (HACS) is involved in the development and maintenance of chronic low back pain (CLBP). The Central Sensitization Inventory (CSI) was developed to evaluate the presence of HACS, with a cut-off value of 4...

Prediction of sustained opioid use in children and adolescents using machine learning.

British journal of anaesthesia
BACKGROUND: Opioid misuse in the paediatric population is understudied. This study aimed to develop a machine learning classifier to differentiate between occasional and sustained opioid users among children and adolescents in outpatient settings.

Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest.

Neurology
BACKGROUND AND OBJECTIVES: In light of limited intensive care capacities and a lack of accurate prognostic tools to advise caregivers and family members responsibly, this study aims to determine whether automated cerebral CT (CCT) analysis allows pro...

Preliminary study on AI-assisted diagnosis of bone remodeling in chronic maxillary sinusitis.

BMC medical imaging
OBJECTIVE: To construct the deep learning convolution neural network (CNN) model and machine learning support vector machine (SVM) model of bone remodeling of chronic maxillary sinusitis (CMS) based on CT image data to improve the accuracy of image d...

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

Frontiers in endocrinology
OBJECTIVE: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtr...

Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning.

Frontiers in immunology
The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF1...

Understanding sexual homicide in Korea using machine learning algorithms.

Behavioral sciences & the law
The current study was conducted to confirm the characteristics in sexual homicide and to explore variables that effectively differentiate sexual homicide and nonsexual homicide. Further, newer methods that have received attention in criminology, such...

Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning.

PloS one
Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the current NCCN Guidelines. Thus, the classification will be associated with th...