Geriatrics

Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer's disease: insights from machine learning analyses and WGCNA.

BACKGROUND: The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease (AD) remains unclear.

ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus.

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation a...

Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.

Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used m...

Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.

BACKGROUND: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (A...

Sulfonic acid functionalized β-amyloid peptide aggregation inhibitors and antioxidant agents for the treatment of Alzheimer's disease: Combining machine learning, computational, in vitro and in vivo approaches.

Alzheimer's disease (AD) is characterized as a neurodegenerative disorder that is caused by plaque formation by accumulating β-amyloid (Aβ), leading t...

Predicting fall parameters from infant skull fractures using machine learning.

When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limite...

Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study.

OBJECTIVES: The future emergence of disease-modifying treatments for dementia highlights the urgent need to identify reliable and easily accessible to...

Adapting to evolving MRI data: A transfer learning approach for Alzheimer's disease prediction.

Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer's D...

Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study.

BACKGROUND: The two most commonly used methods to identify frailty are the frailty phenotype and the frailty index. However, both methods have limitat...

A novel hybrid deep learning framework based on biplanar X-ray radiography images for bone density prediction and classification.

UNLABELLED: This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) imag...

Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients.

OBJECTIVE: The objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepa...

Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation.

We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, us...

The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023.

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the managem...

The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.

INTRODUCTION: Accurate measurement of dietary intake without interfering in natural eating habits is a long-standing problem in nutritional epidemiolo...

Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data.

Osteoporotic femoral neck fractures (OFNFs) pose a significant orthopedic challenge in the elderly population, accounting for up to 40% of all osteopo...

Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: A comprehensive review in general diseases vs Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD), the most prevalent form of dementia, remains enigmatic in its origins despite the widely accepted "amyloid hypot...

DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention c...

Risk prediction model of cognitive performance in older people with cardiovascular diseases: a study of the National Health and Nutrition Examination Survey database.

BACKGROUND AND AIM: Changes in cognitive function are commonly associated with aging in patients with cardiovascular diseases. The objective of this r...

Interpretable machine learning reveals transport of aged microplastics in porous media: Multiple factors co-effect.

Microplastics (MPs) easily migrate into deeper soil layers, posing potential risks to subterranean habitats and groundwater. However, the mechanisms g...

Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.

AIM: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of mach...