Geriatrics

Review Topic: Don't Cancer Doctors Offer Good-Quality Care?

We offer the perspectives of two veterans on the last quarter century of quality improvement efforts in oncology care. We believe that our colleagues deliver high quality care, but challenges remain in demonstrating this assertion to patients, payers, policy makers and to our colleagues. The journey began with the American College of Surgeons (ACOS) establishing minimum standards for cancer surger...

Prevalence and a LASSO-derived prediction model for screening-positive mild cognitive impairment among older adults in Wuhan.

This study aimed to investigate the prevalence of screening-positive mild cognitive impairment (s-MCI) and to develop a parsimonious prediction model using machine learning methods to identify high-risk older adults in Wuhan, China. A total of 2,190 community-dwelling adults aged ≥ 60 years were recruited through multistage cluster sampling from 30 residential committees in 13 districts. MCI scree...

Differential association of psoas and paraspinal muscle volumes with appendicular lean mass index: AI-derived lumbar computed tomography three-dimensional volumetric indices in older Korean Women: an observational study.

STUDY DESIGN: Retrospective cross-sectional study. PURPOSE: To investigate the relationship between sarcopenia-related markers and artificial intellig...

Artificial Intelligence in Burn and Wound Care: Image Analysis, Prediction, and Clinical Integration.

Burn injuries and chronic wounds impose a substantial and growing global health and economic burden, particularly in low- and middle-income countries ...

Comment on "Integrating machine learning into fracture liaison services: Toward precision risk stratification in osteoporosis care: A prospective cohort study".

Development and internal validation of a clinical prediction model for hemodialysis-related headache using LASSO and Boruta feature selection.

A common neurological symptom in dialysis patients is hemodialysis-related headache (HRH), which can result in premature hemodialysis termination and ...

Regional variability in the associations between social and health-related risk factors and memory across Europe.

Interventions targeting social and health-related risk factors are thought to reduce the risk of cognitive decline and dementia in older age. Despite ...

Measurement fidelity, not model complexity, governs individual cognitive trajectory prediction in Parkinson's disease.

Whether individual-level cognitive trajectories in Parkinson's disease are predictable remains unresolved. Here, we provide convergent evidence that m...

Proteomic clocks combined with deep learning phenotypes track eye aging and diseases.

Proteomics represents a powerful but underutilized approach for characterizing eye aging. Here, leveraging data from three large-scale, cross-national...

Association of Staphylococcus aureus bloodstream infection with 7-day incident delirium in critically Ill adults: a propensity-weighted competing risk analysis.

BACKGROUND: Delirium is a frequent manifestation of acute brain dysfunction in critically ill patients with bloodstream infections (BSI). While the as...

End-to-end DOA estimation via self-supervised cascaded DNNs with array errors mitigation.

In this paper, we present a novel cascaded deep neural network architecture for high-precision direction-of-arrival (DOA) estimation in the presence o...

Quantitative sleep EEG identifies CSF core biomarker-related subgroups in Alzheimer's disease.

Early detection and biological characterization of Alzheimer's disease (AD) remain challenging, as current diagnostic approaches rely on invasive cere...

Cascaded Deep Learning enables multimodal brain PET spatial normalization and quantification for Alzheimer's disease.

Semi-quantitative positron emission tomography (PET) analysis, particularly Centiloid and CenTauRz scaling, is essential for Alzheimer's disease (AD) ...

Machine learning and deep learning for neurological disease analysis: A systematic review across five major disorders.

Artificial Intelligence (AI) has become integral to the research of neurological diseases due to the rapid expansion of neuroimaging, clinical, physio...

Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions.

OBJECTIVE: We compared three magnetic resonance imaging (MRI) sequences-native zero echo time (ZTE), deep learning (DL)-chemical shift correction (CSC...

Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification.

As a progressive neurodegenerative disorder, Alzheimer's disease (AD) requires early and accurate diagnosis to delay pathological progression and impr...

Center of mass feedback for joint torque control during perturbed standing: a potential generalizable control approach for balance augmentation in wearable robots.

BACKGROUND: Exoskeletons have the potential to augment balance and decrease fall risk. However, existing balance-augmenting wearable robotic controlle...

Prediction of delirium in trauma patients using interpretable machine learning.

This study aimed to identify key risk factors for delirium in trauma patients and to develop an interpretable machine learning model using routinely a...

Optimizing acquisition time and injected dose in 18F-FDG PET/CT imaging using deep learning: enhancing image protocol efficiency and safety.

BACKGROUND: This study aims to evaluate the effectiveness of deep learning algorithms in simulating standard acquisition time images from shortened ac...

Deep Learning-Derived Retinal Age Detects Cognitive Impairment.

PURPOSE: To investigate the association between deep learning-derived retinal age and cognitive function and to evaluate whether retinal age outperfor...