Geriatrics

Deep learning in the radiologic diagnosis of osteoporosis: a literature review.

OBJECTIVE: Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep lea...

DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework.

Microbial community analysis is an important field to study the composition and function of microbial communities. Microbial species annotation is crucial to revealing microorganisms' complex ecological functions in environmental, ecological and host interactions. Currently, widely used methods can suffer from issues such as inaccurate species-level annotations and time and memory constraints, and...

[Identification of Osteoarthritis Inflamm-Aging Biomarkers by Integrating Bioinformatic Analysis and Machine Learning Strategies and the Clinical Validation].

OBJECTIVE: To identify inflamm-aging related biomarkers in osteoarthritis (OA).

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medi...

Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI.

Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rec...

Effects of Language Differences on Inpatient Fall Detection Using Deep Learning.

This study examined the effects of language differences between Korean and English on the performance of natural language processing in the classifica...

An intentional approach to managing bias in general purpose embedding models.

Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuatio...

Artificial Intelligence in Geriatrics: Riding the Inevitable Tide of Promise, Challenges, and Considerations.

Topic evolution before fall incidents in new fallers through natural language processing of general practitioners' clinical notes.

BACKGROUND: Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture thi...

Ultrafast Brain MRI with Deep Learning Reconstruction for Suspected Acute Ischemic Stroke.

Background Deep learning (DL)-accelerated MRI can substantially reduce examination times. However, studies prospectively evaluating the diagnostic per...

[Evaluation of brain age changes in patients with liver cirrhosis and hepatic encephalopathy with deep learning models based on structural magnetic resonance imaging].

To investigate the brain aging in patients with cirrhosis and hepatic encephalopathy(HE), constructed a prediction model of brain age based on deep l...

Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies and is a major cause of cancer-related mortalities worldwide (Forner et al., 201...

AgeAnnoMO: a knowledgebase of multi-omics annotation for animal aging.

Aging entails gradual functional decline influenced by interconnected factors. Multiple hallmarks proposed as common and conserved underlying denomina...

Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.

Machine learning (ML) algorithms play a crucial role in the early and accurate diagnosis of Alzheimer's Disease (AD), which is essential for effective...

An Interpretable Population Graph Network to Identify Rapid Progression of Alzheimer's Disease Using UK Biobank.

Alzheimer's disease (AD) manifests with varying progression rates across individuals, necessitating the understanding of their intricate patterns of c...

Generative AI Demonstrated Difficulty Reasoning on Nursing Flowsheet Data.

Excessive documentation burden is linked to clinician burnout, thus motivating efforts to reduce burden. Generative artificial intelligence (AI) poses...

Machine learning model for osteoporosis diagnosis based on bone turnover markers.

To assess the diagnostic utility of bone turnover markers (BTMs) and demographic variables for identifying individuals with osteoporosis. A cross-sect...

An Early Detection and Classification of Alzheimer's Disease Framework Based on ResNet-50.

OBJECTIVE: The objective of this study is to develop a more effective early detection system for Alzheimer's disease (AD) using a Deep Residual Networ...

A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology.

Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of...

Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer's Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis.

BACKGROUND: Alzheimer's disease (AD), a major dementia cause, lacks effective treatment. MRI-based hippocampal volume measurement using artificial int...