Latest AI and machine learning research in schizophrenia for healthcare professionals.
Despite years of research, we are still not able to reliably predict who might benefit from electroconvulsive therapy (ECT) treatment. As we exhaust what is possible using traditional statistical analysis, ECT remains a good candidate for machine learning approaches due to the large data sets with data captured through electroencephalography (EEG) and other objective measures. A systematic review ...
BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time.
Brain disorders are often associated with changes in brain structure and function, where functional changes may be due to underlying structural variat...
The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinic...
The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an u...
Federated learning has shown its unique advantages in many different tasks, including brain image analysis. It provides a new way to train deep learni...
Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therap...
BACKGROUND: Large language models (LLMs) have raised both interest and concern in the academic community. They offer the potential for automating lite...
Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ)...
PURPOSE/OBJECTIVES: This study proposes the utilization of a Natural Language Processing tool to create a semantic search engine for dental education ...
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatme...
RATIONALE AND OBJECTIVES: To differentiate early-onset schizophrenia (EOS) from early-onset bipolar disorder (EBD) using surface-based morphometry mea...
The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with...
Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line...
As medical educators grapple with the consistent demand for high-quality assessments, the integration of artificial intelligence presents a novel solu...
Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating synapses on large populations of CNS neurons. PNN u...
Artificial intelligence (AI) has witnessed significant advancements, reshaping various industries, including healthcare. The introduction of ChatGPT b...
Schizophrenia is a profound and enduring mental disorder that imposes significant negative impacts on individuals, their families, and society at larg...
Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) meth...
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability t...