AIMC Topic: Cross-Sectional Studies

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Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China.

PloS one May 14, 2020
Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset of overt clinical complications. Here, we developed neural network models to predict hypertension, h...
Middle Aged Aged, 80 and over Hypertension Deep Learning Young Adult Hyperglycemia Humans China Chronic Disease Adult Female Retina ROC Curve Fundus Oculi Risk Factors Cross-Sectional Studies Dyslipidemias Models, Biological Male Aged
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A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.

The Lancet. Digital health May 12, 2020
BACKGROUND: Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal...
Photography Male Middle Aged Deep Learning China Renal Insufficiency, Chronic Singapore Fundus Oculi Image Interpretation, Computer-Assisted Eye Diseases Algorithms Female Reproducibility of Results Sensitivity and Specificity Cross-Sectional Studies Humans
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Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

The British journal of ophthalmology May 6, 2020
BACKGROUND: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an A...
Slit Lamp Microscopy Retina Diagnostic Tests, Routine False Positive Reactions Reference Standards Aged Diabetic Retinopathy Middle Aged Adult Artificial Intelligence Humans Sensitivity and Specificity Image Processing, Computer-Assisted Algorithms Cross-Sectional Studies Microscopy, Confocal Female Male Predictive Value of Tests Reproducibility of Results Diagnostic Imaging
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Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression.

American journal of ophthalmology May 6, 2020
PURPOSE: To predict the visual field (VF) of glaucoma patients within the central 10° from optical coherence tomography (OCT) measurements using deep learning and tensor regression.
Aged Adult Sensitivity and Specificity Cross-Sectional Studies Intraocular Pressure Humans Retrospective Studies Female Tomography, Optical Coherence Visual Fields Visual Field Tests Optic Nerve Diseases Glaucoma, Open-Angle Male Middle Aged Deep Learning Visual Acuity Vision Disorders Gonioscopy Slit Lamp Microscopy Axial Length, Eye
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A new lower limb portable exoskeleton for gait assistance in neurological patients: a proof of concept study.

Journal of neuroengineering and rehabilitation May 6, 2020
BACKGROUND: Few portable exoskeletons following the assist-as-needed concept have been developed for patients with neurological disorders. Thus, the main objectives of this proof-of-concept study were 1) to explore the safety and feasibility of an ex...
Humans Algorithms Female Adult Proof of Concept Study Exoskeleton Device Physical Therapy Modalities Stroke Rehabilitation Gait Disorders, Neurologic Cross-Sectional Studies Male Middle Aged Robotics Multiple Sclerosis
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Using Deep Learning to Automate Goldmann Applanation Tonometry Readings.

Ophthalmology Apr 25, 2020
PURPOSE: To develop an objective and automated method for measuring intraocular pressure using deep learning and fixed-force Goldmann applanation tonometry (GAT) techniques.
Female Reproducibility of Results Aged Cross-Sectional Studies Humans Tonometry, Ocular Intraocular Pressure Glaucoma Male Prospective Studies Middle Aged Deep Learning ROC Curve
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Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks.

International journal of neural systems Apr 18, 2020
Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box problem"). In this paper, we train a convolutional neural network (CNN)...
Nerve Net Cross-Sectional Studies Deep Learning Big Data Humans Magnetic Resonance Imaging Autism Spectrum Disorder Connectome Brain
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Prediction of physical violence in schizophrenia with machine learning algorithms.

Psychiatry research Apr 13, 2020
Patients with schizophrenia have been shown to have an increased risk for physical violence. While certain features have been identified as risk factors, it has been difficult to integrate these variables to identify violent patients. The present stu...
Schizophrenic Psychology Physical Abuse Predictive Value of Tests Area Under Curve Schizophrenia Violence Adult Humans Cross-Sectional Studies Machine Learning Male Algorithms Middle Aged Female ROC Curve Risk Factors
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Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data.

Journal of child psychology and psychiatry, and allied disciplines Apr 1, 2020
BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.
Substance-Related Disorders Registries Attention Deficit Disorder with Hyperactivity Humans Machine Learning Sweden Risk Factors Cross-Sectional Studies Comorbidity Adolescent Child, Preschool Child
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Creation and Testing of a Deep Learning Algorithm to Automatically Identify and Label Vessels, Nerves, Tendons, and Bones on Cross-sectional Point-of-Care Ultrasound Scans for Peripheral Intravenous Catheter Placement by Novices.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine Mar 17, 2020
OBJECTIVES: We sought to create a deep learning (DL) algorithm to identify vessels, bones, nerves, and tendons on transverse upper extremity (UE) ultrasound (US) images to enable providers new to US-guided peripheral vascular access to identify anato...
Cross-Sectional Studies Point-of-Care Systems Deep Learning Catheters Tendons Humans Algorithms
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