AIMC Topic: Disease Progression
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Machine learning analysis of serum cholesterol's impact on knee osteoarthritis progression.
Scientific reports
Aug 14, 2024
The controversy surrounding whether serum total cholesterol is a risk factor for the graded progression of knee osteoarthritis (KOA) has prompted this study to develop an authentic prediction model using a machine learning (ML) algorithm. The objecti...
Identification and analysis of significant genes in nonalcoholic steatohepatitis-hepatocellular carcinoma transformation: Bioinformatics analysis and machine learning approach.
Molecular immunology
Aug 13, 2024
PURPOSE: Nonalcoholic steatohepatitis (NASH) has been an increasingly significant contributor to hepatocellular carcinoma (HCC). Understanding the progression from NASH to HCC is critical to early diagnosis and elucidating the underlying mechanisms.
Suppressor of Cytokine Signaling Proteins
Gene Expression Regulation, Neoplastic
Non-alcoholic Fatty Liver Disease
Cell Transformation, Neoplastic
Machine Learning
Liver Neoplasms
Prognosis
Humans
Carcinoma, Hepatocellular
Disease Progression
Single-Cell Analysis
Cell Line, Tumor
Computational Biology
From bytes to nephrons: AI's journey in diabetic kidney disease.
Journal of nephrology
Aug 12, 2024
Diabetic kidney disease (DKD) is a significant complication of type 2 diabetes, posing a global health risk. Detecting and predicting diabetic kidney disease at an early stage is crucial for timely interventions and improved patient outcomes. Artific...
The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.
Biomedical engineering online
Aug 8, 2024
Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-identifying patients at high risk of progression is essential for timely and proactive interventions. Bo...
Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression.
Communications biology
Aug 7, 2024
Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 P...
Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.
Journal of glaucoma
Aug 5, 2024
PRCIS: We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends of VF worsening. We then identified...
Aberrant migration features in primary skin fibroblasts of Huntington's disease patients hold potential for unraveling disease progression using an image based machine learning tool.
Computers in biology and medicine
Aug 2, 2024
Huntington's disease (HD) is a complex neurodegenerative disorder with considerable heterogeneity in clinical manifestations. While CAG repeat length is a known predictor of disease severity, this heterogeneity suggests the involvement of additional ...
Computer-aided prognosis of tuberculous meningitis combining imaging and non-imaging data.
Scientific reports
Jul 30, 2024
Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other outcomes, especially when impacted by co-morbidities such as HIV i...
Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks.
Journal of magnetic resonance imaging : JMRI
Jul 29, 2024
This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature o...
Identification of profiles associated with conversions between the Alzheimer's disease stages, using a machine learning approach.
Alzheimer's research & therapy
Jul 26, 2024
BACKGROUND: The identification of factors involved in the conversion across the different Alzheimer's disease (AD) stages is crucial to prevent or slow the disease progression. We aimed to assess the factors and their combination associated with the ...
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