Autism

Label-Free Detection of Biochemical Changes during Cortical Organoid Maturation via Raman Spectroscopy and Machine Learning.

Human cerebral organoids have become valuable tools in neurodevelopment research, holding promise for investigating neurological diseases and reducing drug development costs. However, clinical translation and large-scale production of brain organoids face challenges due to invasive methodologies such as immunohistochemistry and omics that are traditionally used for their investigation. These hinde...

Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.

Protein sequence design is a highly challenging task, aimed at discovering new proteins that are more functional and producible under laboratory conditions than their natural counterparts. Deep learning-based approaches developed to address this problem have achieved significant success. However, these approaches often do not adequately emphasize the functional properties of proteins. In this stud...

Integrating NLP and LLMs to discover biomarkers and mechanisms in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurological condition characterized by cognitive decline, memory loss, and aberrant behaviour. It affects m...

Genetic association studies using disease liabilities from deep neural networks.

The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort st...

Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy.

This study presents a comprehensive approach for optimizing the acquisition, utilization, and maintenance of ABLVR vascular robots in healthcare setti...

The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges....

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learn...

Multi-label segmentation of carpal bones in MRI using expansion transfer learning.

The purpose of this study was to develop a robust deep learning approach trained with a smallMRI dataset for multi-label segmentation of all eight car...

Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer.

Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time...

AI and early diagnostics: mapping fetal facial expressions through development, evolution, and 4D ultrasound.

The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and e...

A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD.

BACKGROUND: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children and adolescents characterized by persi...

Differentiating Functional Connectivity Patterns in ADHD and Autism Among the Young People: A Machine Learning Solution.

OBJECTIVE: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elemen...

Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.

The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential...

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.

Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classifica...

Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder.

Although the efficacy of pharmacy in the treatment of attention deficit/hyperactivity disorder (ADHD) has been well established, the lack of predictor...

Transfer Learning Prediction of Early Exposures and Genetic Risk Score on Adult Obesity in Two Minority Cohorts.

Due to ethnic heterogeneity in genetic architecture, genetic risk score (GRS) constructed within the European population generally possesses poor port...

Prenatal Diagnostics Using Deep Learning: A Dual Approach to Plane Localization and Cerebellum Segmentation in Ultrasound Images.

OBJECTIVE: The fetal ultrasound examination is the significant task of mid-term pregnancy inspection and the accurate localization as well as the segm...

Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.

IMPORTANCE: Decreasing mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients'...

AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions.

Autism Spectrum Disorder (ASD) is a neurological condition, with recent statistics from the CDC indicating a rising prevalence of ASD diagnoses among ...

High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI.

Choroid plexus (CP) is an important brain structure that produces cerebrospinal fluid (CSF). CP perfusion has been studied using multi-delay arterial ...