AIMC Topic: Disease Progression

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Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [F]FDG PET study.

European radiology
OBJECTIVES: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to A...

Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis.

International journal of biological macromolecules
In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functi...

Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.

Oral diseases
OBJECTIVES: Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes.

Comparison of Manual vs Artificial Intelligence-Based Muscle MRI Segmentation for Evaluating Disease Progression in Patients With CMT1A.

Neurology
BACKGROUND AND OBJECTIVES: Intramuscular fat fraction (FF), assessed with quantitative MRI (qMRI), has emerged as one of the few responsive outcome measures in CMT1A patients. The main limitation for its use in future therapeutic trials is the time r...

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression.

International journal of molecular sciences
Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using adv...

Development of a machine learning model for precision prognosis of rapid kidney function decline in people with diabetes and chronic kidney disease.

Diabetes research and clinical practice
AIMS: To develop a machine learning model for predicting rapid kidney function decline in people with type 2 diabetes (T2D) and chronic kidney disease (CKD) and to pinpoint key modifiable risk factors for targeted interventions.

Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning.

Computers in biology and medicine
PURPOSE: Clinical validation of "BrainLossNet", a deep learning-based method for fast and robust estimation of brain volume loss (BVL) from longitudinal T1-weighted MRI, for the detection of accelerated BVL in multiple sclerosis (MS) and for the disc...

Identification of key biomarkers for predicting atherosclerosis progression in polycystic ovary syndrome via bioinformatics analysis and machine learning.

Computers in biology and medicine
OBJECTIVE: Polycystic ovary syndrome (PCOS) is one of the most significant cardiovascular risk factors, playing vital roles in various cardiovascular diseases such as atherosclerosis (AS). This study attempted to explore key biomarkers for predicting...

Serum metabolite biomarkers for the early diagnosis and monitoring of age-related macular degeneration.

Journal of advanced research
INTRODUCTION: Age-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide, with significant challenges for early diagnosis and treatment.