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Optimizing Concussion Care Seeking: Using Machine Learning to Predict Delayed Concussion Reporting.

The American journal of sports medicine
BACKGROUND: Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, me...

Using deep learning to improve the intelligibility of a target speaker in noisy multi-talker environments for people with normal hearing and hearing loss.

The Journal of the Acoustical Society of America
Understanding speech in noisy environments is a challenging task, especially in communication situations with several competing speakers. Despite their ongoing improvement, assistive listening devices and speech processing approaches still do not per...

Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models.

Investigative ophthalmology & visual science
PURPOSE: AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, selection of artificial intelligence (AI) architecture is an important variable in model development. H...

Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.

JCO clinical cancer informatics
PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documenta...

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.

CNS neuroscience & therapeutics
MAIN PROBLEM: Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it enco...

Development and validation of a machine learning-based model to predict isolated post-challenge hyperglycemia in middle-aged and elder adults: Analysis from a multicentric study.

Diabetes/metabolism research and reviews
INTRODUCTION: Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes patients, which leads to the misdiagnosis of patients with isolated post-challenge hyperglycemia (IPH),...

Potential Role of Generative Adversarial Networks in Enhancing Brain Tumors.

JCO clinical cancer informatics
PURPOSE: Contrast enhancement is necessary for visualizing, diagnosing, and treating brain tumors. Through this study, we aimed to examine the potential role of general adversarial neural networks in generating artificial intelligence-based enhanceme...

Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning.

Investigative ophthalmology & visual science
PURPOSE: Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, in intermediate AMD (iAMD) using high-precision artificial intelligence (AI) quantifications on opti...

Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network.

Translational vision science & technology
PURPOSE: To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA).