AIMC Topic: Aged

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Machine learning-based identification of small RNA signatures in aqueous humor as a step toward precision diagnosis of glaucoma.

Annals of medicine
BACKGROUND: Glaucoma is a progressive neurodegenerative disease of the optic nerve and one of the leading causes of irreversible blindness worldwide. Small RNAs (including miRNAs) play an important role in the pathogenesis of the disease. Despite ext...

Investigating the relationship between blood factors and HDL-C levels in the bloodstream using machine learning methods.

Journal of health, population, and nutrition
INTRODUCTION: The study investigates the relationship between blood lipid components and metabolic disorders, specifically high-density lipoprotein cholesterol (HDL-C), which is crucial for cardiovascular health. It uses logistic regression (LR), dec...

Machine learning-based prediction of N2 lymph node metastasis in non-small cell lung cancer.

BMC pulmonary medicine
BACKGROUND: Lung cancer is a leading cause of cancer-related mortality worldwide. Accurate staging of mediastinal lymph nodes is a crucial step in determining appropriate treatment approaches. Current noninvasive diagnostic methods do not provide suf...

AI-driven chemotoxicity prediction in colorectal cancer: impact of race, SDOH, and biological aging.

BMC cancer
BACKGROUND: Patients with colorectal cancer (CRC) often experience chemotoxicity that impacts treatment adherence, survival, and quality of life. Early screening for chemotoxicity risk is vital, yet comprehensive predictive models are lacking. The ob...

The diagnostic value of serum cysteine protease inhibitor (CST4) in colorectal cancer: a preliminary study.

BMC gastroenterology
BACKGROUND: CST4 is associated with various cancers but its diagnostic value in colorectal cancer (CRC) has not been clearly established. This study aims to further validate the diagnostic value of CST4 in colorectal cancer using random forest models...

Support vector machine-based preoperative identification of IDH-Mutant low-grade gliomas in adult gliomas using clinical features.

BMC neurology
BACKGROUND: The preoperative identification of (isocitrate dehydrogenase) IDH-mutant low-grade gliomas (LGGs) is critical for personalized treatment planning. We aimed to develop a streamlined machine-learning model using key clinical features for ra...

Predicting hematologic toxicity in advanced cervical cancer patients using interpretable machine learning models based on radiomics and dosimetrics.

BMC cancer
BACKGROUND AND OBJECTIVES: Hematologic toxicity (HT) is a common and serious side effect for advanced cervical cancer patients undergoing chemoradiotherapy. Accurately predicting HT can significantly improve patient management and treatment outcomes....

Machine learning-assisted screening of clinical features for predicting difficult-to-treat rheumatoid arthritis.

Scientific reports
To identify clinical features that predict the risk of meeting difficult-to-treat (D2T) rheumatoid arthritis (RA) definition in advance. This retrospective analysis included RA patients from the ATTRA registry who initiated biologic (b-) or targeted ...

Deep learning prediction of peak oxygen uptake in patients with coronary heart disease: a retrospective study.

BMJ open
OBJECTIVE: To develop and validate prediction models for peak oxygen uptake (VO₂peak) in patients with coronary heart disease (CHD) using submaximal cardiopulmonary exercise testing (CPET) indicators and deep learning methods.

Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.

Journal of cancer research and clinical oncology
PURPOSE: The integration of artificial intelligence (AI) with hyperspectral imaging (HSI) offers a promising avenue for improving pre-therapeutic prognosis, a key factor in optimizing cancer treatment strategies. This study explores the potential of ...