AIMC Topic: Young Adult

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Deep learning COVID-19 detection bias: accuracy through artificial intelligence.

International orthopaedics
BACKGROUND: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported re...

Visual Speech Recognition: Improving Speech Perception in Noise through Artificial Intelligence.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVES: To compare speech perception (SP) in noise for normal-hearing (NH) individuals and individuals with hearing loss (IWHL) and to demonstrate improvements in SP with use of a visual speech recognition program (VSRP).

The interplay between multisensory integration and perceptual decision making.

NeuroImage
Facing perceptual uncertainty, the brain combines information from different senses to make optimal perceptual decisions and to guide behavior. However, decision making has been investigated mostly in unimodal contexts. Thus, how the brain integrates...

Neural activity underlying the detection of an object movement by an observer during forward self-motion: Dynamic decoding and temporal evolution of directional cortical connectivity.

Progress in neurobiology
Relatively little is known about how the human brain identifies movement of objects while the observer is also moving in the environment. This is, ecologically, one of the most fundamental motion processing problems, critical for survival. To study t...

Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability.

Archives of physical medicine and rehabilitation
OBJECTIVE: To assess the utility of applying natural language processing (NLP) to electronic health records (EHRs) to identify individuals with chronic mobility disability.

Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.

Human brain mapping
PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized f...

Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks.

Physics in medicine and biology
Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background parenchymal enhancement (BPE) in magnetic resonance imaging (MRI), which is useful for breast cancer risk assessment. In this study, we develop an auto...

Macroscale and microcircuit dissociation of focal and generalized human epilepsies.

Communications biology
Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at the level of whole-brain mechanisms. Using multimodal imaging, connectomics, and computational simulat...

A self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS).

BMC neurology
BACKGROUND: Cognitive impairment is common in patients with multiple sclerosis (MS). Accurate and repeatable measures of cognition have the potential to be used as markers of disease activity.

An automated machine learning approach to predict brain age from cortical anatomical measures.

Human brain mapping
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a m...