AIMC Topic: Biomarkers

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Neural-network classification of cardiac disease from P cardiovascular magnetic resonance spectroscopy measures of creatine kinase energy metabolism.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive...

A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG.

PloS one
Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. However, there exist few automatic methods to detect CHD at an early stage. A challenge in the developm...

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.

Artificial intelligence in medicine
Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosi...

Validation of oxidative stress assay for schizophrenia.

Schizophrenia research
Accumulating evidence implicates oxidative stress in a range of diseases, yet no objective measurement has emerged that characterizes the global nature of oxidative stress. Previously, we reported a measurement that employs the moderately strong oxid...

Machine learning and glioma imaging biomarkers.

Clinical radiology
AIM: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring.

Automated detection and classification of early AMD biomarkers using deep learning.

Scientific reports
Age-related macular degeneration (AMD) affects millions of people and is a leading cause of blindness throughout the world. Ideally, affected individuals would be identified at an early stage before late sequelae such as outer retinal atrophy or exud...

Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions.

Scientific reports
A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other slee...

Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis.

Journal of dental research
This study's objectives were to test correlations among groups of biomarkers that are associated with condylar morphology and to apply artificial intelligence to test shape analysis features in a neural network (NN) to stage condylar morphology in te...

Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients.

Computational and mathematical methods in medicine
The diagnostic performance of an artificial neural network model for chronic HBV-induced liver fibrosis reverse is not well established. Our research aims to construct an ANN model for estimating noninvasive predictors of fibrosis reverse in chronic ...

Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown.