Latest AI and machine learning research in geriatrics for healthcare professionals.
BACKGROUND AND OBJECTIVE: Mild cognitive impairment (MCI) is a transitional state between normal aging and Alzheimer's disease (AD), and accurately predicting the progression trend of MCI is critical to the early prevention and treatment of AD. Brain structural magnetic resonance imaging (sMRI), as one of the most important biomarkers for the diagnosis of AD, has been applied in various deep learn...
Hierarchical reinforcement learning (HRL) is a promising approach to perform long-horizon goal-reaching tasks by decomposing the goals into subgoals. In a holistic HRL paradigm, an agent must autonomously discover such subgoals and also learn a hierarchy of policies that uses them to reach the goals. Recently introduced end-to-end HRL methods accomplish this by using the higher-level policy in the...
BACKGROUND: Falls are a major problem associated with ageing. Yet, fall-risk classification models identifying older adults at risk are lacking. Curre...
Dementia affects the patient's memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a cl...
Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by...
With the increasing number of aged population and growing burden of healthy aging demands, a rational standard for evaluation aging is in urgent need....
The ageing population has led to a surge in the adoption of artificial intelligence (AI) technologies in elderly healthcare worldwide. However, in the...
Osteoporosis contributes significantly to health and economic burdens worldwide. However, the development of osteoporosis-related prediction tools has...
PURPOSE: We present a systematic literature review of dialogue agents for Artificial Intelligence (AI) and agent-based conversational systems dealing ...
Radiologists today play a central role in making diagnostic decisions and labeling images for training and benchmarking artificial intelligence (AI) a...
BACKGROUND: Sarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning-based segmen...
Shortage of labor and increased work of young people are causing problems in terms of care and welfare of a growing proportion of elderly people. This...
Abstract-domain adaptation action recognition is a hot research topic in machine learning and some effective approaches have been proposed. However, s...
Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). ...
Effectively predicting protein toxicity plays an essential step in the early stage of protein-based drug discovery, which is of great help to speed up...
Deep neural networks have been successfully applied to generate predictive patterns from medical and diagnostic data. This paper presents an approach ...
Vision-based localization approaches now underpin newly emerging navigation pipelines for myriad use cases, from robotics to assistive technologies. C...
Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has bee...
Distributed fiber optic sensing (DFS) systems are an effective method for long-distance pipeline safety inspections. Highly accurate vibration signal ...
Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics...