Psychiatry

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

PURPOSE: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is de...

Analysis of robot-specific operative time and surgical team anxiety level and its effect on alignment during robot-assisted TKA.

Adapting to robotic-assisted (RA) total knee arthroplasty (TKA) is hindered by the surgeon's fear of extra time. The main purpose of this study was to determine the robot's operative time, and the secondary goals were to assess the surgical team's anxiety, implant location and size, and limb alignment. From February to April 2022, 40 participants participated in prospective research. The study inc...

Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet.

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...

Prediction of suicidal ideation among preadolescent children with machine learning models: A longitudinal study.

BACKGROUND: Machine learning (ML) has been widely used to predict suicidal ideation (SI) in adolescents and adults. Nevertheless, studies of accurate ...

Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization.

Sex plays a crucial role in human brain development, aging, and the manifestation of psychiatric and neurological disorders. However, our understandin...

Qhali: A Humanoid Robot for Assisting in Mental Health Treatment.

In recent years, social assistive robots have gained significant acceptance in healthcare settings, particularly for tasks such as patient care and mo...

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to s...

Effectiveness and safety of robot-assisted versus fluoroscopy-assisted cortical bone trajectory screw instrumentation in spinal surgery: a systematic review and meta-analysis.

Robot-assisted (RA) technology has been shown to be a safe aid in spine surgery, this meta-analysis aims to compare surgical parameters and clinical i...

Optimization strategy of community planning for environmental health and public health in smart city under multi-objectives.

As population density increases, environmental hygiene and public health become increasingly severe. As the space where residents stay for the longest...

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG.

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagn...

Predicting suicide risk in real-time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box.

BACKGROUND: This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated ...

Monitoring Mental Health: Legal and Ethical Considerations of Using Artificial Intelligence in Psychiatric Wards.

Artificial intelligence (AI) is being tested and deployed in major hospitals to monitor patients, leading to improved health outcomes, lower costs, an...

A deep learning approach for mental health quality prediction using functional network connectivity and assessment data.

While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approa...

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability t...

Using deep neural networks to disentangle visual and semantic information in human perception and memory.

Mental representations of familiar categories are composed of visual and semantic information. Disentangling the contributions of visual and semantic ...

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques.

BACKGROUND: The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typical...

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorde...

Capacity of Generative AI to Interpret Human Emotions From Visual and Textual Data: Pilot Evaluation Study.

BACKGROUND: Mentalization, which is integral to human cognitive processes, pertains to the interpretation of one's own and others' mental states, incl...

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review.

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classifica...

Large Language Models and Healthcare Alliance: Potential and Challenges of Two Representative Use Cases.

Large language models (LLMS) emerge as the most promising Natural Language Processing approach for clinical practice acceleration (i.e., diagnosis, pr...