AIMC Topic: Case-Control Studies

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Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm.

International journal of molecular sciences Feb 1, 2020
Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. To facilitate its implementation in metabolic diagnostic screening, we developed a method for automat...
Humans Metabolome Data Interpretation, Statistical Tandem Mass Spectrometry Knowledge Bases Metabolism, Inborn Errors Algorithms Biomarkers Case-Control Studies Mass Screening
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Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning.

Acta ophthalmologica Jan 24, 2020
PURPOSE: Recent advances in deep learning have seen an increase in its application to automated image analysis in ophthalmology for conditions with a high prevalence. We wanted to identify whether deep learning could be used for the automated classif...
Adult Case-Control Studies Tomography, Optical Coherence Humans Male Female Stargardt Disease Middle Aged Severity of Illness Index Deep Learning ROC Curve Image Processing, Computer-Assisted
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Machine learning detection of Atrial Fibrillation using wearable technology.

PloS one Jan 24, 2020
BACKGROUND: Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stro...
Atrial Fibrillation Diagnosis, Computer-Assisted Wearable Electronic Devices Electrocardiography Support Vector Machine Humans Aged Case-Control Studies
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Improvement of classification performance of Parkinson's disease using shape features for machine learning on dopamine transporter single photon emission computed tomography.

PloS one Jan 24, 2020
OBJECTIVE: To assess the classification performance between Parkinson's disease (PD) and normal control (NC) when semi-quantitative indicators and shape features obtained on dopamine transporter (DAT) single photon emission computed tomography (SPECT...
Case-Control Studies ROC Curve Parkinson Disease Area Under Curve Corpus Striatum Tomography, Emission-Computed, Single-Photon Humans Machine Learning Databases, Factual
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A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine Jan 22, 2020
PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer's disease (AD) are geriatric diseases and common causes of dementia. Recently, many studies on the segmentation, disease detection, or classification of MRI using deep learning ha...
Magnetic Resonance Imaging Diagnosis, Differential Alzheimer Disease Pattern Recognition, Automated Humans Diagnosis, Computer-Assisted Male Neuroimaging Artificial Intelligence Deep Learning Hydrocephalus, Normal Pressure Middle Aged Reproducibility of Results Imaging, Three-Dimensional Disease Progression Aged Image Processing, Computer-Assisted Case-Control Studies Aged, 80 and over Brain Female
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Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

The lancet. Gastroenterology & hepatology Jan 22, 2020
BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system...
Early Diagnosis Clinical Alarms Diagnosis, Computer-Assisted Male Double-Blind Method Deep Learning Humans Mass Screening Female Middle Aged Adenoma Colonic Polyps China Adult Colonoscopy Observer Variation Incidence Colorectal Neoplasms Case-Control Studies
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Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.

European journal of cancer (Oxford, England : 1990) Jan 20, 2020
BACKGROUND: Deep learning convolutional neural networks (CNNs) show great potential for melanoma diagnosis. Melanoma thickness at diagnosis among others depends on melanoma localisation and subtype (e.g. advanced thickness in acrolentiginous or nodul...
Humans Neural Networks, Computer Prognosis ROC Curve Follow-Up Studies Retrospective Studies Male Female Melanoma Middle Aged Aged Case-Control Studies Deep Learning
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Deep learning-based automated speech detection as a marker of social functioning in late-life depression.

Psychological medicine Jan 16, 2020
BACKGROUND: Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device ...
Female Aged Aged, 80 and over Male Aging Humans Social Interaction England Social Adjustment Wearable Electronic Devices Depressive Disorder, Major Attention Case-Control Studies Neuropsychological Tests Deep Learning Speech
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Preliminary experience with an image-free handheld robot for total knee arthroplasty: 77 cases compared with a matched control group.

European journal of orthopaedic surgery & traumatology : orthopedie traumatologie Jan 16, 2020
BACKGROUND: Achieving an optimal limb alignment is an important factor affecting the long-term survival of total knee arthroplasty (TKA). This is the first study to look at the limb alignment and orientation of components in TKA using a novel image-f...
Radiography Surgery, Computer-Assisted Knee Joint Arthroplasty, Replacement, Knee Middle Aged Postoperative Complications Case-Control Studies Robotics Image Interpretation, Computer-Assisted Outcome and Process Assessment, Health Care Bone Malalignment Humans Female Male
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Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach.

Scientific reports Jan 15, 2020
This study describes a segmentation-free deep learning (DL) algorithm for measuring retinal nerve fibre layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT). The study included 25,285 B-scans from 1,338 eyes of 706 subjects....
Nerve Fibers Retinal Ganglion Cells Visual Fields Humans Case-Control Studies Cross-Sectional Studies Algorithms Glaucoma Deep Learning Male Female Image Processing, Computer-Assisted Middle Aged Tomography, Optical Coherence
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