Geriatrics

Advancing Alzheimer's disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study.

OBJECTIVES: Alzheimer's disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited accessibility restrict practical application. This study aimed to develop a convenience, efficient prediction model for AD risk using machine learning techn...

Analyzing patterns of frequent mental distress in Alzheimer's patients: A generative AI approach.

This study tackles creating Python code for beginners with generative AI and analyzing trends in mental distress among Alzheimer's patients in the US (2015-2022 CDC data). It guides beginners through using AI to generate code for visualizing these trends by age and sex. The findings reveal females, particularly those 50-64 years old, experience the highest rates of mental distress. This emphasizes...

Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS).

BACKGROUND: Due to the ageing population and evolving lifestyles occurring in China, middle-aged and elderly populations have become high-risk groups ...

Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.

BACKGROUND: Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline, SCD) are considered risk states of dementia, such...

Integrating manual annotation with deep transfer learning and radiomics for vertebral fracture analysis.

BACKGROUND: Vertebral compression fractures (VCFs) are prevalent in the elderly, often caused by osteoporosis or trauma. Differentiating acute from ch...

Community Graph Convolution Neural Network for Alzheimer's Disease Classification and Pathogenetic Factors Identification.

As a complex neural network system, the brain regions and genes collaborate to effectively store and transmit information. We abstract the collaborati...

Joint ensemble learning-based risk prediction of Alzheimer's disease among mild cognitive impairment patients.

OBJECTIVE: Due to the recognition for the importance of early intervention in Alzheimer's disease (AD), it is important to focus on prevention and tre...

Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis.

BACKGROUND: Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerg...

Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.

Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces t...

Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset.

BACKGROUND: Medical imaging techniques for diagnosing sarcopenia have been extensively investigated. Studies have proposed using the T-score and patie...

Identifying natural inhibitors against FUS protein in dementia through machine learning, molecular docking, and dynamics simulation.

Dementia, a complex and debilitating spectrum of neurodegenerative diseases, presents a profound challenge in the quest for effective treatments. The ...

RotInv-PCT: Rotation-Invariant Point Cloud Transformer via feature separation and aggregation.

The widespread use of point clouds has spurred the rapid development of neural networks for point cloud processing. A crucial property of these networ...

GTIGNet: Global Topology Interaction Graphormer Network for 3D hand pose estimation.

Estimating 3D hand poses from monocular RGB images presents a series of challenges, including complex hand structures, self-occlusions, and depth ambi...

Development and usability evaluation of a nurse-led clinical decision support system (AI-AntiDelirium) for management of intensive care unit delirium: A mixed methods study.

BACKGROUND: Clinical decision support systems (CDSS) have been identified to aid clinical decision-making, but few studies focus on the application of...

Deep learning opportunistic screening for osteoporosis and osteopenia using radiographs of the foot or ankle - A pilot study.

BACKGROUND: The gold standard method for diagnosing low bone mineral density (BMD) is using dual-energy X-ray absorptiometry (DXA) however, most patie...

End-To-End Deep Learning Explains Antimicrobial Resistance in Peak-Picking-Free MALDI-MS Data.

Mass spectrometry is used to determine infectious microbial species in thousands of clinical laboratories across the world. The vast amount of data al...

Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer's disease progression.

The diagnosis of Alzheimer's disease (AD) based on visual features-informed by clinical knowledge has achieved excellent results. Our study endeavors ...

Development of a machine learning model and a web application for predicting neurological outcome at hospital discharge in spinal cord injury patients.

BACKGROUND: Spinal cord injury (SCI) is a devastating condition with profound physical, psychological, and socioeconomic consequences. Despite advance...

Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer's disease and frontotemporal dementia.

BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are both progressive neurological disorders that affect the elderly. Distinguis...

Self-supervised parametric map estimation for multiplexed PET with a deep image prior.

Multiplexed positron emission tomography (mPET) imaging allows simultaneous observation of physiological and pathological information from multiple tr...