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Use of deep learning model for paediatric elbow radiograph binomial classification: initial experience, performance and lessons learnt.

Singapore medical journal
INTRODUCTION: In this study, we aimed to compare the performance of a convolutional neural network (CNN)-based deep learning model that was trained on a dataset of normal and abnormal paediatric elbow radiographs with that of paediatric emergency dep...

A deep learning-based ensemble for autism spectrum disorder diagnosis using facial images.

PloS one
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder leading to an inability to socially communicate and in extreme cases individuals are completely dependent on caregivers. ASD detection at early ages is crucial as early detection can red...

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ign...

Improving Quality of Life of Families Headed by Parents With Intellectual Disabilities and Their Children by Means of Assistive Social Robotics.

Journal of applied research in intellectual disabilities : JARID
BACKGROUND: Families of parents with intellectual disabilities still face discrimination, stigma and inadequate support, placing them in vulnerable positions. Social assistive robotics offers promising support. This study investigates the possible im...

Fast and effective assessment for individuals with special needs form optimization and prediction models.

BMC psychology
The aim of this study was to determine which items in the psychological assessment forms used by counselling and research centres for individuals with special needs are effective in classifying individuals into special needs diagnostic categories. Da...

A machine learning model for predicting severe mycoplasma pneumoniae pneumonia in school-aged children.

BMC infectious diseases
OBJECTIVE: To develop an interpretable machine learning (ML) model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in order to provide reliable factors for predicting the clinical type of the disease.

MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.

Malaria journal
BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagnosed using microscopy or rapid diagnostic tests (RDTs), which have limited sensitivity, and microsco...

Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.

BMC medical informatics and decision making
BACKGROUND: Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacerbation events significantly impact children's health and quality of life. Machine learning, an advanced data analysis technique, has shown great poten...

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Pediatric respiratory diseases, including asthma and pneumonia, are major causes of morbidity and mortality in children. Auscultation of lung sounds is a key diagnostic tool but is prone to subjective variability. The integration of artif...

Calibration and Validation of Machine Learning Models for Physical Behavior Characterization: Protocol and Methods for the Free-Living Physical Activity in Youth (FLPAY) Study.

JMIR research protocols
BACKGROUND: Wearable activity monitors are increasingly used to characterize physical behavior. The development and validation of these characterization methods require criterion-labeled data typically collected in a laboratory or simulated free-livi...