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Exploring and Identifying Key Factors in Predicting Dyslexia in Children: Advanced Machine Learning Algorithms From Screening to Diagnosis.

Clinical psychology & psychotherapy
INTRODUCTION: The current study aimed to develop and validate a machine learning (ML)-based predictive models for early dyslexia detection in children by integrating neurocognitive, linguistic and behavioural predictors.

Reliability of Emotion Analysis from Human Facial Expressions Using Multi-task Cascaded Convolutional Neural Networks.

The journal of medical investigation : JMI
Life support robots in care settings must be able to read a person's emotions from facial expressions to achieve empathic communication. This study aims to determine the degree of agreement between Multi-task Cascaded Convolutional Neural Networks (M...

Using the Minnesota Multiphasic Personality Inventory-2 restructured form to predict functioning after treatment for borderline personality disorder: A machine learning approach.

Psychological assessment
Insight into predictors of functioning after treatment for borderline personality disorder (BPD) is limited, despite growing recognition that more focus on other aspects of recovery, especially psychosocial functioning, is warranted. The present stud...

[Comparison of the Impact of Deep Learning Techniques on Low-noise Head CT Images].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: This study aims to compare the effects of two types of deep learning (DL) techniques on brain CT values, image noise content, and contrast-to-noise ratio (CNR) between white and gray matter in low-noise head CT images, along with adaptive it...

Virtual Monochromatic Imaging of Half-Iodine-Load, Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction in Patients with Renal Insufficiency: A Clinical Pilot Study.

Journal of Nippon Medical School = Nippon Ika Daigaku zasshi
BACKGROUND: We retrospectively examined image quality (IQ) of thin-slice virtual monochromatic imaging (VMI) of half-iodine-load, abdominopelvic, contrast-enhanced CT (CECT) by dual-energy CT (DECT) with deep learning image reconstruction (DLIR).

mGNN-bw: Multi-Scale Graph Neural Network Based on Biased Random Walk Path Aggregation for ASD Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In recent years, computationally assisted diagnosis for classifying autism spectrum disorder (ASD) and typically developing (TD) individuals based on neuroimaging data, such as functional magnetic resonance imaging (fMRI), has garnered significant at...

Optimizing Stroke Detection Using Evidential Networks and Uncertainty-Based Refinement.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Evaluating neurological impairments post-stroke is essential for assessing treatment efficacy and managing subsequent disabilities. Conventional clinical assessment methods depend largely on clinicians' visual and physical evaluations, resulting in c...

Improving fMRI-Based Autism Severity Identification via Brain Network Distance and Adaptive Label Distribution Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Machine learning methodologies have been profoundly researched in the realm of autism spectrum disorder (ASD) diagnosis. Nonetheless, owing to the ambiguity of ASD severity labels and individual differences in ASD severity, current fMRI-based methods...

Analyzing Demographic Grocery Purchase Patterns in Kenyan Supermarkets Through Unsupervised Learning Techniques.

Inquiry : a journal of medical care organization, provision and financing
Kenya is experiencing a significant increase in the prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, hypertension, Type 2 diabetes, and certain cancers (bowel, lung, prostate, and uterine). This case is not unique to Ke...