AIMC Topic: Cross-Sectional Studies
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Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study.
Osteoarthritis and cartilage
Feb 23, 2021
OBJECTIVE: To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-o...
The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers.
Radiography (London, England : 1995)
Feb 20, 2021
INTRODUCTION: The current technological developments in medical imaging are centred largely on the increasing integration of artificial intelligence (AI) into all equipment modalities. This survey assessed the perspectives of African radiographers on...
A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.
PloS one
Feb 19, 2021
BACKGROUND: Identifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in hi...
Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning.
BMC infectious diseases
Feb 18, 2021
BACKGROUND: Coronavirus disease 2019 (COVID-19) has caused a global pandemic that has raised worldwide concern. This study aims to investigate the correlation between the extent of lung infection and relevant clinical laboratory testing indicators in...
The Use of Machine Learning Techniques to Determine the Predictive Value of Inflammatory Biomarkers in the Development of Type 2 Diabetes Mellitus.
Metabolic syndrome and related disorders
Feb 16, 2021
Certain inflammatory biomarkers, such as interleukin-6, interleukin-1, C-reactive protein (CRP), and fibrinogen, are prototypical acute-phase parameters that can also be predictors of cardiovascular disease. However, this inflammatory response can a...
Robot-assisted rehabilitation of hand function after stroke: Development of prediction models for reference to therapy.
Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Feb 16, 2021
BACKGROUND: Recovery of hand function after stroke represents the hardest target for clinicians. Robot-assisted therapy has been proved to be effective for hand recovery. Nevertheless, studies aimed to refer patients to the best therapy are missing.
Radiographers' perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study.
Journal of medical radiation sciences
Feb 14, 2021
INTRODUCTION: The integration of artificial intelligence (AI) systems into medical imaging is advancing the practice and patient care. It is thought to further revolutionise the entire field in the near future. This study explored Ghanaian radiograph...
Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study.
American journal of ophthalmology
Feb 9, 2021
PURPOSE: To compare the performance of a novel convolutional neural network (CNN) classifier and human graders in detecting angle closure in EyeCam (Clarity Medical Systems, Pleasanton, California, USA) goniophotographs.
Specialization
Anterior Eye Segment
Ophthalmologists
Glaucoma, Angle-Closure
Gonioscopy
Male
Middle Aged
Diagnosis, Computer-Assisted
China
Expert Systems
Asian
Photography
Image Processing, Computer-Assisted
Area Under Curve
Reproducibility of Results
Aged
Aged, 80 and over
Cross-Sectional Studies
Humans
Neural Networks, Computer
Retrospective Studies
Female
Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis.
Ophthalmology. Retina
Feb 1, 2021
PURPOSE: To develop a deep learning (DL) system that can detect referable diabetic retinopathy (RDR) and vision-threatening diabetic retinopathy (VTDR) from images obtained on ultra-widefield scanning laser ophthalmoscope (UWF-SLO).
Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning.
Ophthalmology. Retina
Jan 28, 2021
PURPOSE: To evaluate the predictive usefulness of quantitative imaging biomarkers, acquired automatically from OCT scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti-...
Cross-Sectional Studies
Male
Middle Aged
Deep Learning
Retrospective Studies
Female
Aged
Aged, 80 and over
Vascular Endothelial Growth Factor A
Intravitreal Injections
Ranibizumab
Wet Macular Degeneration
Angiogenesis Inhibitors
Follow-Up Studies
Visual Acuity
Tomography, Optical Coherence
Humans
Prognosis
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