AIMC Topic: Aged

Clear Filters Showing 131 to 140 of 14227 articles

Mammo-AGE: deep learning estimation of breast age from mammograms.

Nature communications
Biological age is an important indicator of organ functions and health. Although mammograms are widely used in breast cancer screening, the potential of mammogram-based biological age predictors remains underexplored. Here, we propose a deep learning...

Automatic detection of urinary stones from non-contrast enhanced computed tomography images.

Urolithiasis
Urinary stones, one of the most common emergency conditions, traverse the ureter, urine flow is obstructed, resulting in hydronephrosis and severe pain. However, vessel wall calcifications or phleboliths are frequently observed in abdominal and pelvi...

High Concordance Between GPT-4o and Multidisciplinary Tumor Board Decisions in Breast Cancer: A Retrospective Decision Support Analysis.

Journal of medical systems
Large language models (LLMs) such as ChatGPT have gained attention for their potential to assist clinical decision-making in oncology. However, real-world validation of these models against multidisciplinary tumor board (MTB) recommendations-particul...

Machine learning model of clinical laboratory data for 30-day mortality of patients with hodgkin's lymphoma in ICU: a retrospective study based on MIMIC-IV database.

Clinical and experimental medicine
Prognostic stratification of Hodgkin lymphoma (HL) patients in ICU remains challenging, with conventional scoring systems often overlooking pathophysiological biomarkers. This retrospective cohort study analyzed 1,908 HL patients from the MIMIC-IV da...

SleepPPG-Net2: deep learning generalization for sleep staging from photoplethysmography.

Physiological measurement
. sleep staging is essential for diagnosing sleep disorders and managing sleep health. Traditional methods require time-consuming manual scoring. Recent photoplethysmography (PPG)-based deep learning models perform well on local datasets but struggle...

Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning.

European journal of medical research
BACKGROUND: Age-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unval...

The effects of physical activity on diabetic retinopathy in type 2 diabetes using automated vascular analysis: a cohort study.

Journal of global health
BACKGROUND: Evidence regarding the association between physical activity (PA) and diabetic retinopathy (DR) remains inconsistent. Furthermore, its effects on retinal vessel diameters in type 2 diabetes are not well established. We aimed to investigat...

More insights into disruption and decoupling of individual metabolic connectomes in Parkinson's disease.

Progress in neuro-psychopharmacology & biological psychiatry
PURPOSE: Metabolic disturbances are hallmark pathological features of Parkinson's disease (PD) and can be noninvasively captured by arterial spin labeling (ASL). However, the metabolic pattern of disconnections beyond regional alterations remains sca...

An explainable machine learning-based approach to predicting treatment response for neurofeedback in ADHD.

Scientific reports
Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with serious long-term effects if untreated, emphasizing the need for early treatment given its neurobiological heterogeneity. This study introduces a novel expla...

Faecal metabolites as a readout of habitual diet capture dietary interactions with the gut microbiome.

Nature communications
The interplay between diet and gut microbiome composition is complex. Faecal metabolites, the end products of human and microbial metabolism, provide insights into these interactions. Here, we integrate faecal metabolomics, metagenomics, and habitual...