AIMC Topic: Middle Aged

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Multimodal AI and tumour microenvironment integration predicts metastasis in cutaneous melanoma.

Nature communications
Accurate prognostication is essential to guide clinical management in localised cutaneous melanoma (CM), the form of skin cancer with the highest mortality. While the tumour microenvironment (TME) plays a key role in disease progression, current stag...

Development and external validation of a machine learning model to predict high flow nasal cannula failure.

BMJ open respiratory research
INTRODUCTION: High-flow nasal cannula (HFNC) is an important treatment option for acute hypoxic respiratory failure and can improve outcomes. However, patients on a prolonged duration of HFNC have worse clinical outcomes and increased mortality. It i...

AI-driven pre-screening for colorectal cancer using complete blood counts: toward broader population impact.

International journal of colorectal disease
PURPOSE: Early colorectal cancer (CRC) detection is crucial for effective treatment; however, traditional screening methods face challenges. Colonoscopy, though highly effective, has limited availability, and fecal immunochemical tests (FIT) are more...

Predicting distant metastasis in early-onset kidney cancer using machine learning: a SEER database study with external validation.

Clinical and experimental medicine
Patients with early-onset kidney cancer (EOKC) face a marked decline in prognosis after distant metastasis, yet the accuracy of current predictive methods remains limited. This study aims to develop a predictive model using multiple machine learning ...

Impact of imaging biomarkers from body composition analysis on outcome of endovascularly treated acute ischemic stroke patients.

Journal of neurointerventional surgery
BACKGROUND: We investigate the association of imaging biomarkers extracted from fully automated body composition analysis (BCA) of computed tomography (CT) angiography images of endovascularly treated acute ischemic stroke (AIS) patients regarding an...

A novel deep learning system for STEMI prognostic prediction from multi-sequence cardiac magnetic resonance.

Science bulletin
ST-elevation myocardial infarction (STEMI) remains a leading cause of cardiovascular morbidity and mortality worldwide, and accurate early risk stratification is critical for implementing precision therapies in clinical practice. However, existing cl...

C-reactive protein-triglyceride glucose index in predicting three-vessel coronary artery disease risk: a retrospective study using machine learning approaches.

Annals of medicine
BACKGROUND: Three-vessel coronary artery disease (TVD) is a severe subtype of coronary heart disease, strongly associated with inflammation and metabolic dysfunction. The C-reactive protein-triglyceride glucose index (CTI), an integrated measure of i...

Variation in the efficiency of English general practices and associated factors: A cross-sectional study of 5069 general practices.

The European journal of general practice
BACKGROUND: Healthcare demand in English general practice exceeds supply, necessitating practice efficiency. To our knowledge, no study has explored factors associated with practice efficiency in England using a quality-adjusted output.

Machine learning combined with body composition predicts surgical difficulty in mid-low rectal cancer surgery.

Annals of medicine
BACKGROUND: This study sought to identify critical body composition characteristics associated with surgical difficulty in Laparoscopic Total Mesorectal Excision (LaTME) and to develop and validate an interpretable machine learning model using body c...

Effect of upper-limb robot-assisted therapy combined with pneumatic gloves on upper limb function in young and middle-aged stroke patients: a pilot randomized controlled trial.

Journal of neuroengineering and rehabilitation
BACKGROUND: This study aimed to evaluate the effects of an end-effector-type upper-limb robot-assisted therapy (UL-RAT) combined with pneumatic gloves (PGs) on improving upper limb function in young and middle-aged stroke patients.