AIMC Topic: Cross-Sectional Studies

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Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma.

The British journal of ophthalmology Jun 27, 2020
BACKGROUND/AIM: To train and validate the prediction performance of the deep learning (DL) model to predict visual field (VF) in central 10° from spectral domain optical coherence tomography (SD-OCT).
Gonioscopy Nerve Fibers Retinal Ganglion Cells Visual Fields Visual Field Tests Female Aged Cross-Sectional Studies Intraocular Pressure Male Middle Aged Deep Learning Predictive Value of Tests Tomography, Optical Coherence Humans Glaucoma
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Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach.

Behavioural neurology Jun 22, 2020
The present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy controls. The data for this cross-sectional study were collected from 200 diagnosed OCD patients and ...
Obsessive-Compulsive Disorder Male Psychiatric Status Rating Scales Child Factor Analysis, Statistical Humans Neural Networks, Computer Female Reproducibility of Results Cross-Sectional Studies
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Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions.

European journal of cancer (Oxford, England : 1990) Jun 10, 2020
BACKGROUND: Convolutional neural networks (CNNs) have shown a dermatologist-level performance in the classification of skin lesions. We aimed to deliver a head-to-head comparison of a conventional image analyser (CIA), which depends on segmentation a...
Humans Databases, Factual Prospective Studies Prognosis Reproducibility of Results Aged Aged, 80 and over Young Adult Adult Female Melanoma Skin Neoplasms Dermoscopy Deep Learning Adolescent Predictive Value of Tests Image Interpretation, Computer-Assisted Cross-Sectional Studies Male Middle Aged Diagnosis, Computer-Assisted
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Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks.

The British journal of ophthalmology Jun 4, 2020
AIMS: To investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tom...
Polyps Choroid Diseases Neural Networks, Computer Retrospective Studies Cross-Sectional Studies Visual Acuity Choroid Humans Tomography, Optical Coherence Fundus Oculi Fluorescein Angiography Wet Macular Degeneration
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Altered resting-state functional connectivity and effective connectivity of the habenula in irritable bowel syndrome: A cross-sectional and machine learning study.

Human brain mapping Jun 3, 2020
Irritable bowel syndrome (IBS) is a disorder involving dysfunctional brain-gut interactions characterized by chronic recurrent abdominal pain, altered bowel habits, and negative emotion. Previous studies have linked the habenula to the pathophysiolog...
Young Adult Adult Cross-Sectional Studies Male Middle Aged Humans Female Thalamus Irritable Bowel Syndrome Dorsolateral Prefrontal Cortex Periaqueductal Gray Support Vector Machine Habenula Magnetic Resonance Imaging Feasibility Studies Pain Connectome
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S100 proteins, cytokines, and chemokines as tear biomarkers in children with juvenile idiopathic arthritis-associated uveitis.

Ocular immunology and inflammation May 28, 2020
PURPOSE: Biomarkers for juvenile idiopathic arthritis-associated uveitis (JIA-U) are needed. We aimed to measure inflammatory biomarkers in tears as a non-invasive method to identify biomarkers of uveitis activity.
Uveitis Intercellular Adhesion Molecule-1 Male Arthritis, Juvenile Adolescent Eye Proteins Child Enzyme-Linked Immunosorbent Assay Interleukin-8 Humans S100A12 Protein Biomarkers Tears Female Cross-Sectional Studies
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Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.

Epilepsia May 20, 2020
OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics.
Socioeconomic Factors Age Factors Humans Cluster Analysis Sex Factors Cross-Sectional Studies Female Unsupervised Machine Learning Age of Onset Epilepsy Male Adult Patient Reported Outcome Measures Psychology Middle Aged Quality of Life
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Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study.

Scientific reports May 20, 2020
To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjust...
Cross-Sectional Studies Male Cardiovascular Diseases Middle Aged Case-Control Studies Diabetes Mellitus, Type 2 Glomerular Filtration Rate Follow-Up Studies Magnetic Resonance Imaging Humans Machine Learning Prospective Studies Female Models, Neurological Organ Size Linear Models Gray Matter Germany Prediabetic State Life Style Confounding Factors, Epidemiologic
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Supervised machine learning for coronary artery lumen segmentation in intravascular ultrasound images.

International journal for numerical methods in biomedical engineering May 19, 2020
Intravascular ultrasound (IVUS) has been widely used to capture cross sectional lumen frames of inner wall of coronary arteries. This kind of medical imaging modalities is capable of providing detailed and significant information of lumen contour sha...
Ultrasonography, Interventional Supervised Machine Learning Coronary Vessels Humans Algorithms Cross-Sectional Studies Ultrasonography
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Health Care Employees' Perceptions of the Use of Artificial Intelligence Applications: Survey Study.

Journal of medical Internet research May 14, 2020
BACKGROUND: The advancement of health care information technology and the emergence of artificial intelligence has yielded tools to improve the quality of various health care processes. Few studies have investigated employee perceptions of artificial...
Artificial Intelligence Health Personnel Female Young Adult Adult Humans Cross-Sectional Studies Male Middle Aged Surveys and Questionnaires Workplace
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