AIMC Topic: Longitudinal Studies
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Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study.
Journal of medical Internet research
May 27, 2021
BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19.
Deep recurrent model for individualized prediction of Alzheimer's disease progression.
NeuroImage
May 13, 2021
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developin...
Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma.
Scientific reports
May 13, 2021
Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promisi...
Novel AI driven approach to classify infant motor functions.
Scientific reports
May 10, 2021
The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA)...
Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study.
Journal of medical Internet research
May 6, 2021
BACKGROUND: The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented "infodemic"; the velocity and volume of data production h...
Deep learning approach to predict pain progression in knee osteoarthritis.
Skeletal radiology
Apr 9, 2021
OBJECTIVE: To develop and evaluate deep learning (DL) risk assessment models for predicting pain progression in subjects with or at risk of knee osteoarthritis (OA).
Longitudinal diffusion MRI analysis using Segis-Net: A single-step deep-learning framework for simultaneous segmentation and registration.
NeuroImage
Mar 29, 2021
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear regi...
Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease.
Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
Mar 29, 2021
Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying tre...
Early identification of epilepsy surgery candidates: A multicenter, machine learning study.
Acta neurologica Scandinavica
Mar 26, 2021
OBJECTIVES: Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates befo...
Active, continual fine tuning of convolutional neural networks for reducing annotation efforts.
Medical image analysis
Mar 24, 2021
The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such larg...
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