AIMC Topic: Aged

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Demographic bias of expert-level vision-language foundation models in medical imaging.

Science advances
Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit trainin...

Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally validated with the ANSWER cohort.

Arthritis research & therapy
BACKGROUND: Undifferentiated arthritis (UA) often develops into rheumatoid arthritis (RA), but predicting disease progression from seronegative UA remains challenging because seronegative RA often does not meet the classification criteria. This study...

Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.

BMC public health
OBJECTIVE: The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern in recent years. Machine learning meth...

Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.

BMC infectious diseases
OBJECTIVE: Bloodstream infection (BSI) is a significant cause of mortality in patients with hematologic malignancies(HMs), particularly amid rising antibiotic resistance. This study aimed to analyze pathogen distribution, drug-resistance patterns and...

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.

BMC medical imaging
BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focu...

Preliminary evaluation of ChatGPT model iterations in emergency department diagnostics.

Scientific reports
Large language model chatbots such as ChatGPT have shown the potential in assisting health professionals in emergency departments (EDs). However, the diagnostic accuracy of newer ChatGPT models remains unclear. This retrospective study evaluated the ...

Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning.

Scientific reports
Tai Chi, an Asian martial art, is renowned for its health benefits, particularly in promoting healthy aging among older adults, improving balance, and reducing fall risk. However, methodological challenges hinder the objective measurement of adherenc...

AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study.

Scientific reports
Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a critical life support technology for severely ill patients. Despite its benefits, patients face high costs and significant mortality risks. To improve clinical decision-making, this stu...

Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning.

Geospatial health
INTRODUCTION: Chronic Obstructive Pulmonary Disease (COPD) mortality rates and global warming have been in the focus of scientists and policymakers in the past decade. The long-term shifts in temperature and weather patterns, commonly referred to as ...

Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach.

PloS one
Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes disfiguring of the affected extremities, often leading to permanent disability and stigma. Described as a disease of poverty, the impact of socioeconomic indicators ...