AIMC Topic: Middle Aged

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Joint impact of stress hyperglycaemic ratio and glycaemic variability in patients with ischaemic stroke and machine learning for mortality prediction.

BMC neurology
BACKGROUND: The global burden of ischaemic stroke (IS) is high, which is potentially relevant to stress hyperglycemia ratio (SHR) and glycaemic variability (GV). This study aims to evaluate the combined effect of the SHR and GV with predict short-ter...

Cell-free DNA methylation and fragmentomics-based liquid biopsy for accurate esophageal cancer detection.

BMC cancer
BACKGROUND: Cell-free DNA is a promising source of biomarkers for early cancer detection and carries tumor-driven methylation and fragmentation features that have achieved good diagnostic efficacy across various cancers. However, there were no studie...

Machine learning-based clinical-radiomics model for predicting recurrence risk after radical surgery in sinonasal squamous cell carcinoma: a preliminary 2-year follow-up study.

BMC medical imaging
BACKGROUND: To construct and validate an optimal machine learning (ML)-based clinical-radiomics model integrating clinical and radiomics features for predicting recurrence risk within 2 years after radical surgery in patients with sinonasal squamous ...

Pre-operatively predicting kidney stone recurrence: integrating radiomic features and clinical variables using machine learning.

BMC medical imaging
BACKGROUND: Radiomics and artificial intelligence have shown strong predictive capabilities in urinary stone research, particularly concerning stone composition, characteristics, and treatment outcomes. However, the association of stone radiomics and...

NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.

Scientific reports
Brain age prediction based on anatomical MRI scans, as an essentially new measure in neuroimaging and aging research, provides a crucial marker for the early diagnosis of neurodegenerative diseases, cognitive health appraisal, and biological age pred...

Explainable ensemble learning for Epstein-Barr virus risk prediction in ulcerative colitis and Crohn's disease using routine biomarkers.

Scientific reports
Epstein-Barr virus (EBV) exacerbates inflammatory bowel disease (IBD) and is challenging to monitor with invasive or costly tests. We investigated whether explainable machine learning can predict EBV infection from routine clinical data in ulcerative...

Classifying schizophrenia subtypes via resting-state EEG complexity networks.

Scientific reports
Schizophrenia (SZ) is increasingly recognized as a network disorder marked by abnormal functional connectivity, yet the clinical utility of fMRI remains limited. Electroencephalography (EEG) provides a more practical alternative, though conventional ...

Design and development of a portable multiwavelength LED-based diffuse reflectance spectroscopy tool for rapid breast cancer identification.

Scientific reports
Breast cancer is the most prevalent cancer among women worldwide, emphasizing the need for rapid and accurate diagnostic tools to improve patient outcomes and survival rates. In this study, we developed a diagnostic tool-a multispectral pen based on ...

Identification of novel biomarkers for epithelial ovarian cancer through machine learning and explainable artificial intelligence using in silico and in vitro analysis.

Scientific reports
Epithelial ovarian cancer (EOC) is a lethal gynecological malignancy. Ongoing research aimed to identify novel biomarkers and develop combined algorithms to improve diagnosis and prognosis prediction for EOC. RNA-seq related to EOC were obtained from...

Opportunistic screening of type 2 diabetes with deep metric learning using electronic health records.

Scientific reports
Deep learning models leveraging electronic health records (EHR) for opportunistic screening of type 2 diabetes (T2D) can improve current practices by identifying individuals who may need further glycemic testing. Accurate onset prediction and subtypi...