AI Medical Compendium Topic:
Disease Progression

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Using Relevance Feedback to Distinguish the Changes in EEG During Different Absence Seizure Phases.

Clinical EEG and neuroscience
We carried out a series of statistical experiments to explore the utility of using relevance feedback on electroencephalogram (EEG) data to distinguish between different activity states in human absence epilepsy. EEG recordings from 10 patients with ...

Artificial neural networks in neurosurgery.

Journal of neurology, neurosurgery, and psychiatry
Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literatu...

Bridging scales in cancer progression: mapping genotype to phenotype using neural networks.

Seminars in cancer biology
In this review we summarise our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its pheno...

Association of Deep Learning-based Chest CT-derived Respiratory Parameters with Disease Progression in Amyotrophic Lateral Sclerosis.

Radiology
Background Forced vital capacity (FVC) is a standard measure of respiratory function in patients with amyotrophic lateral sclerosis (ALS) but has limitations, particularly for patients with bulbar impairment. Purpose To determine the value of deep le...

Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning.

JAMA psychiatry
IMPORTANCE: The diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence or early adulthood, which impedes initiation of targeted treatment.

Deep Learning for Predicting Acute Exacerbation and Mortality of Interstitial Lung Disease.

Annals of the American Thoracic Society
Some patients with interstitial lung disease (ILD) have a high mortality rate or experience acute exacerbation of ILD (AE-ILD) that results in increased mortality. Early identification of these high-risk patients and accurate prediction of the onset...

Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis.

World journal of gastroenterology
BACKGROUND: Hepatic fibrosis (HF) represents a pivotal stage in the progression and potential reversal of cirrhosis, underscoring the importance of early identification and therapeutic intervention to modulate disease trajectory.

Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.

Human brain mapping
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to t...

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.

Schizophrenia bulletin
BACKGROUND AND HYPOTHESIS: Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of ...

Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop models that predict which patients with glaucoma will progress to require surgery, combining structured data from electronic health records (EHRs) and retinal fiber layer optical coherence tomography ...