AIMC Topic: Cross-Sectional Studies
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The effects of light touch cue on gait initiation in patients with Parkinson's disease.
Journal of bodywork and movement therapies
Sep 2, 2020
INTRODUCTION: One of the common impairments in patients with Parkinson's disease (PD) is disturbance of gait initiation. A light touch cue improves postural stability in patients with PD. Little is known about the effects of a light touch cue on gait...
Importance of general adiposity, visceral adiposity and vital signs in predicting blood biomarkers using machine learning.
International journal of clinical practice
Aug 26, 2020
INTRODUCTION: Blood biomarkers are measured for their ability to characterise physiological and disease states. Much is known about linear relations between blood biomarker concentrations and individual vital signs or adiposity indexes (eg, BMI). Com...
Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Aug 26, 2020
PURPOSE: To develop a deep learning method to predict visual field (VF) from wide-angle swept-source optical coherence tomography (SS-OCT) and compare the performance of three Google Inception architectures.
Automated detection of early-stage ROP using a deep convolutional neural network.
The British journal of ophthalmology
Aug 23, 2020
BACKGROUND/AIM: To automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN).
Female
Infant, Newborn
Sensitivity and Specificity
Cross-Sectional Studies
Neural Networks, Computer
Algorithms
Retrospective Studies
Birth Weight
Infant, Premature
Infant, Low Birth Weight
Gestational Age
Male
Diagnosis, Computer-Assisted
ROC Curve
Image Processing, Computer-Assisted
Humans
Retinopathy of Prematurity
Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries.
BMJ open
Aug 16, 2020
OBJECTIVES: To explore the efficacy of machine learning (ML) techniques in predicting under-five mortality (U5M) in low-income and middle-income countries (LMICs) and to identify significant predictors of U5M.
Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study.
Journal of medical Internet research
Aug 14, 2020
BACKGROUND: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining...
Identifying Facial Features and Predicting Patients of Acromegaly Using Three-Dimensional Imaging Techniques and Machine Learning.
Frontiers in endocrinology
Jul 29, 2020
Facial changes are common among nearly all acromegalic patients. As they develop slowly, patients often fail to notice such changes before they become obvious. Consequently, diagnosis and treatment are often delayed. So far, convenient and accurate ...
Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data.
Annals of emergency medicine
Jul 24, 2020
STUDY OBJECTIVE: Acute kidney injury occurs commonly and is a leading cause of prolonged hospitalization, development and progression of chronic kidney disease, and death. Early acute kidney injury treatment can improve outcomes. However, current dec...
A decision support system based on support vector machine for diagnosis of periodontal disease.
BMC research notes
Jul 13, 2020
OBJECTIVE: Early diagnosis of many diseases is essential for their treatment. Furthermore, the existence of abundant and unknown variables makes more complicated decision making. For this reason, the diagnosis and classification of diseases using mac...
Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison.
European radiology
Jun 29, 2020
OBJECTIVE: This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing ker...
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