Psychiatry

Hybrid CNN and random forest model with late fusion for detection of autism spectrum disorder in Toddlers.

Accurate and early diagnosis of Autism Spectrum Disorder (ASD) in toddlers is crucial for effective intervention. Traditional models have shown limited success, while deep learning approaches achieve higher accuracies. Our study proposes a hybrid model combining VGG16, a pre-trained deep CNN, with an RF classifier to leverage high-level image feature extraction using the ACD image dataset on Kaggl...

Psychometric properties and Turkish adaptation of the artificial intelligence attitude scale (AIAS-4): evidence for construct validity.

Artificial intelligence (AI) attitude scales can be used to better evaluate the benefit and drawback cons of AI. This article consists of two different studies examining attitudes towards AI. In Study I (N = 370), the four-item Artificial Intelligence Attitude Scale-4 (AIAS-4) has a one-dimensional structure as a result of confirmatory factor analysis and the fit index values ​​are at an acceptabl...

An ensemble approach using multidimensional convolutional neural networks in wavelet domain for schizophrenia classification from sMRI data.

Schizophrenia is a complicated mental condition marked by disruptions in thought processes, perceptions, and emotional responses, which can cause seve...

The role of generative artificial intelligence in psychiatric education- a scoping review.

BACKGROUND: The growing prevalence of mental health conditions, worsened by the COVID-19 pandemic, highlights the urgent need for enhanced psychiatric...

Artificial Intelligence and Qualitative Analysis of Emergency Department Telemental Health Care Implementation Survey.

: Implementation of telemental health care in emergency departments (EDs) in the United States (U.S.) has been increasing. Artificial intelligence (AI...

Perceived Barriers and Facilitators of Use of Artificial Intelligence in Eating Disorder Care: A Commentary on Linardon et al. (2025).

Artificial intelligence (AI) has the potential to revolutionize mental health care, including for eating disorders, but there are still a number of co...

Automated ADHD detection using dual-modal sensory data and machine learning.

This study explores using dual-modal sensory data and machine learning to objectively identify Attention-Deficit/Hyperactivity Disorder (ADHD), a neur...

Two-tier nature inspired optimization-driven ensemble of deep learning models for effective autism spectrum disorder diagnosis in disabled persons.

Autism spectrum disorder (ASD) includes a varied set of neuropsychiatric illnesses. This disorder is described by a definite grade of loss in social c...

Explainability Enhanced Machine Learning Model for Classifying Intellectual Disability and Attention-Deficit/Hyperactivity Disorder With Psychological Test Reports.

BACKGROUND: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability ...

An interpretable deep-learning approach to detect biomarkers in anxious-depressed symptoms from prefrontal fNIRS signals during an autobiographical memory test.

BACKGROUND: Individuals with anxious-depressed (AD) symptoms have more severe mood disorders and cognitive impairment than those with non-anxious depr...

Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients.

BACKGROUND: Depression is a common complication after a stroke that may lead to increased disability and decreased quality of life. The objective of t...

Machine Learning of Smoking Relapse: the Role of Racial Differences and E-Cigarette Vaping Characteristics on Former Smokers.

BACKGROUND: Machine learning models can help identify multifaceted factors influencing tobacco use transitions. A random forest model is developed to ...

Day-to-day dynamics of facial emotion expressions in posttraumatic stress disorder.

Facial expressions are an essential component of emotions that may reveal mechanisms maintaining posttraumatic stress disorder (PTSD). However, most r...

Capsule DenseNet++: Enhanced autism detection framework with deep learning and reinforcement learning-based lifestyle recommendation.

Autism Spectrum Disorder (ASD) is a complex neurological condition that impairs the ability to interact, communicate, and behave. It is becoming incre...

Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review.

The development of digital instruments for mental health monitoring using biosensor data from wearable devices can enable remote, longitudinal, and ob...

Association of Psychological Resilience With Decelerated Brain Aging in Cognitively Healthy World Trade Center Responders.

BACKGROUND: Despite their exposure to potentially traumatic stressors, the majority of World Trade Center (WTC) responders-those who worked on rescue,...

Should my Chatbot Health Coach be Certified and Regulated?

Artificial intelligence shows both perils and promises as a way to address the difficulties in accessing professional support such as health coaching ...

Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data.

Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress...

Deep Learning-Assisted SERS for Therapeutic Drug Monitoring of Clozapine in Serum on Plasmonic Metasurfaces.

Clozapine is widely regarded as one of the most effective therapeutics for treatment-resistant schizophrenia. Despite its proven efficacy, the therape...

Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.

Mental disorders are a representative type of brain disorder, including anxiety, major depressive depression (MDD), and autism spectrum disorder (ASD)...