AIMC Topic: Disease Progression
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Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study.
BMC musculoskeletal disorders
Nov 8, 2024
BACKGROUND: Predicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This study aimed to develop an artificial intelligence (AI) model to predict hip OA progression via plain r...
Prediction and clustering of Alzheimer's disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data.
Scientific reports
Nov 4, 2024
Early detection of Alzheimer's disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The ob...
Understanding the role of machine learning in predicting progression of osteoarthritis.
The bone & joint journal
Nov 1, 2024
AIMS: Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prev...
Applying stacking ensemble method to predict chronic kidney disease progression in Chinese population based on laboratory information system: a retrospective study.
PeerJ
Nov 1, 2024
BACKGROUND AND OBJECTIVE: Chronic kidney disease (CKD) is a major public health issue, and accurate prediction of the progression of kidney failure is critical for clinical decision-making and helps improve patient outcomes. As such, we aimed to deve...
Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [F]FDG PET study.
European radiology
Oct 31, 2024
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
Oct 31, 2024
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
Oct 27, 2024
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
Oct 24, 2024
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
Oct 22, 2024
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
Oct 19, 2024
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.
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