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Classification of Parkinson's disease severity using gait stance signals in a spatiotemporal deep learning classifier.

Medical & biological engineering & computing
Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease...

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.

Translational psychiatry
Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention ha...

Consistency of the Signature of Phonotraumatic Vocal Hyperfunction Across Different Ambulatory Voice Measures.

Journal of speech, language, and hearing research : JSLHR
PURPOSE: Although different factors and voice measures have been associated with phonotraumatic vocal hyperfunction (PVH), it is unclear what percentage of individuals with PVH exhibit such differences during their daily lives. This study used a mach...

The development of artificial intelligence in the histological diagnosis of Inflammatory Bowel Disease (IBD-AI).

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Inflammatory bowel disease (IBD) includes Crohn's Disease (CD) and Ulcerative Colitis (UC). Correct diagnosis requires the identification of precise morphological features such basal plasmacytosis. However, histopathological interpretatio...

Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVE: Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detect...

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

Journal of affective disorders
BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presenta...

Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis.

International journal of molecular sciences
Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of s...

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach.

International journal of molecular sciences
Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation...

An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health.

BMC medical informatics and decision making
BACKGROUND: Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan.