Geriatrics

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network.

Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability with illness progresses. At present, the diagnosis of AD mainly depends on the interviews between patients and doctors, which is slow, expensive, and subjective, so it is not a better solution to recognize AD using the currently available neuropsychological examinations and clinical diagnostic criter...

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representing height-normalized SMA, can serve as markers of sarcopenia. Normal SMA and SMI values have been reported primarily in adults. The purpose of this study was to use an automated deep learning (DL) pipeline for muscle segmentation on abdominal CT to d...

Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study.

BACKGROUND: Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, aut...

Visual attention prediction improves performance of autonomous drone racing agents.

Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select ...

Transfer learning for data-efficient abdominal muscle segmentation with convolutional neural networks.

BACKGROUND: Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emerging imaging biomarker of patient frailty. Data an...

A CT image feature space (CTIS) loss for restoration with deep learning-based methods.

Deep learning-based methods have been widely used in medical imaging field such as detection, segmentation and image restoration. For supervised learn...

Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic.

A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representati...

Care robot research and development plan for disability and aged care in Korea: A mixed-methods user participation study.

The population of Korea is aging rapidly, and this has led to a care burden for caregivers. Without adequate caregivers to address the increased burde...

Prediction of clinical trial enrollment rates.

Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long st...

Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling.

Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for effective treatment before irreversible myocardial p...

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things.

Computer vision is one of the hottest research directions in artificial intelligence at present, and its research goal is to give computers the abilit...

Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.

Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heteroge...

Application of Fuzzy and Rough Logic to Posture Recognition in Fall Detection System.

Considering that the population is aging rapidly, the demand for technology for aging-at-home, which can provide reliable, unobtrusive monitoring of h...

Recognition of Bookmark Aging Degree Based on Probabilistic Neural Network.

Bookmarks are the basis for librarians to get books on and off shelves and borrowers to borrow books. In order to solve the problem of time-consuming ...

Predictive classification of Alzheimer's disease using brain imaging and genetic data.

For now, Alzheimer's disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the ...

Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).

BACKGROUND: Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We ...

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data.

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipr...

Parallax attention stereo matching network based on the improved group-wise correlation stereo network.

Recent stereo matching methods, especially end-to-end deep stereo matching networks, have achieved remarkable performance in the fields of autonomous ...

Artificial intelligence model to identify elderly patients with locomotive syndrome: A cross-section study.

BACKGROUND: Identifying elderly individuals with locomotive syndrome is important to prevent disability in this population. Although screening tools f...

Machine learning outperforms clinical experts in classification of hip fractures.

Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population age...