AIMC Topic: Disease Progression
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AI-facilitated home monitoring for cystic fibrosis exacerbations across pediatric and adult populations.
Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
Feb 18, 2025
BACKGROUND: AI-aided home stethoscopes offer the opportunity of continuous remote monitoring of cystic fibrosis (CF) patients, reducing the need for clinic visits.
Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.
Neural networks : the official journal of the International Neural Network Society
Feb 15, 2025
While numerous studies strive to exploit the complementary potential of MRI and PET using learning-based methods, the effective fusion of the two modalities remains a tricky problem due to their inherently distinctive properties. In addition, current...
Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series.
NPJ systems biology and applications
Feb 15, 2025
Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but the...
Progression-Free Survival Prediction Performance of ChatGPT: Analysis With Real Life Data in Early and Locally Advanced Prostate Cancer.
The Prostate
Feb 13, 2025
OBJECTIVE: To evaluate the progression-free survival (PFS) time in patients with early-stage and locally advanced prostate cancer and to compare the estimates provided by ChatGPT with actual survival data.
A quantitatively interpretable model for Alzheimer's disease prediction using deep counterfactuals.
NeuroImage
Feb 13, 2025
Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions. Counterfactual reasoning has rec...
Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning.
Alzheimer's research & therapy
Feb 13, 2025
BACKGROUND: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treatment modes. We aimed to develop a deep learning model to predict cognitive conversion to guide re-as...
Analysis and validation of programmed cell death genes associated with spinal cord injury progression based on bioinformatics and machine learning.
International immunopharmacology
Feb 9, 2025
BACKGROUND: Spinal cord injury (SCI) is a severe condition affecting the central nervous system. It is marked by a high disability rate and potential for death. Research has demonstrated that programmed cell death (PCD) plays a significant role in th...
Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.
Nature communications
Feb 8, 2025
Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell c...
Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.
The journal of prevention of Alzheimer's disease
Feb 6, 2025
BACKGROUND: Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline, SCD) are considered risk states of dementia, such as Alzheimer's Disease (AD). However, it is challenging to accurately predict conversion from norma...
Machine learning-based plasma metabolomics for improved cirrhosis risk stratification.
BMC gastroenterology
Feb 6, 2025
BACKGROUND: Cirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). The rapid development of metabolomic technologies has enabled the capture of metabolic changes related to the progression of cirrhosis.
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