AIMC Topic: Humans

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Construction of a deep learning-based predictive model to evaluate the influence of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts.

Biomedical engineering online
BACKGROUND: Matrix metalloproteinase-2 (MMP-2) secretion homeostasis, governed by the multifaceted interplay of skin stretching, is a pivotal determinant influencing wound healing dynamics. This investigation endeavors to devise an artificial intelli...

Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases.

BMC endocrine disorders
The role of genetic susceptibility in early warning and precise treatment of diabetic kidney disease (DKD) requires further investigation. A case-control study was conducted to evaluate the predictive effect of GSK3B genetic polymorphisms on the susc...

Comparison of responses from different artificial intelligence-powered chatbots regarding the All-on-four dental implant concept.

BMC oral health
BACKGROUND: Recent advancements in Artificial Intelligence (AI) have transformed the healthcare field, particularly through chatbots like ChatGPT, OpenEvidence, and MediSearch. These tools analyze complex data to aid clinical decision-making, enhanci...

Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.

BMC medical informatics and decision making
BACKGROUND: Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients.

A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models.

BMC medical informatics and decision making
BACKGROUND AND OBJECTIVE: The machine learning (ML) models for acute myocardial infarction (AMI) are considered to have better predictive ability for mortality compared to conventional risk scoring models. However, previous ML prediction models have ...

A radiogenomics study on F-FDG PET/CT in endometrial cancer by a novel deep learning segmentation algorithm.

BMC cancer
OBJECTIVE: To create an automated PET/CT segmentation method and radiomics model to forecast Mismatch repair (MMR) and TP53 gene expression in endometrial cancer patients, and to examine the effect of gene expression variability on image texture feat...

StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images.

BMC medical imaging
BACKGROUND AND OBJECTIVE: Stroke ranks among the leading causes of disability and death worldwide. Timely detection can reduce its impact. Machine learning delivers powerful tools for image‑based diagnosis. This study introduces StrokeNeXt, a lightwe...

Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms.

BMC neurology
BACKGROUND: Glioma is a common primary malignant brain tumor. This study aimed to develop a predictive model for glioma risk by these screened key SNPs in the Chinese Han population.

Research on ischemic stroke risk assessment based on CTA radiomics and machine learning.

BMC medical imaging
BACKGROUND: The study explores the value of a model constructed by integrating CTA-based carotid plaque radiomic features, clinical risk factors, and plaque imaging characteristics for prognosticating the risk of ischemic stroke.