Neurology

Autism

Latest AI and machine learning research in autism for healthcare professionals.

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Accuracy of machine learning in preoperative identification of genetic mutation status in lung cancer: A systematic review and meta-analysis.

BACKGROUND AND PURPOSE: We performed this systematic review and meta-analysis to investigate the per...

Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of : A Pilot Integrative Machine Learning Study.

Suicide is a major public health problem caused by a complex interaction of various factors. Major d...

Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.

The ongoing expansion of human genomic datasets propels therapeutic target identification; however, ...

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for dis...

HFSCCD: A Hybrid Neural Network for Fetal Standard Cardiac Cycle Detection in Ultrasound Videos.

In the fetal cardiac ultrasound examination, standard cardiac cycle (SCC) recognition is the essenti...

Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review.

BACKGROUND: Human Emotion Recognition (HER) has been a popular field of study in the past years. Des...

Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is char...

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impair...

Discrimination of Genetic Biomarkers of Disease through Machine-Learning-Based Hypothesis Testing of Direct SERS Spectra of DNA and RNA.

Cancer is globally a leading cause of death that would benefit from diagnostic approaches detecting ...

Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model.

BACKGROUND AND OBJECTIVE: People with autism spectrum disorder (ASD) often have cognitive impairment...

Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification.

In recent years, research has been demonstrating that movement analysis, utilizing machine learning ...

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling.

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, po...

Early identification of autism spectrum disorder based on machine learning with eye-tracking data.

BACKGROUND: Early identification of autism spectrum disorder (ASD) improves long-term outcomes, yet ...

Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning.

Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's ...

Boosting predictive models and augmenting patient data with relevant genomic and pathway information.

The recurrence of low-stage lung cancer poses a challenge due to its unpredictable nature and divers...

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