AIMC Topic: Child

Clear Filters Showing 1411 to 1420 of 3433 articles

Computer-aided diagnosis of autism spectrum disorder from EEG signals using deep learning with FAWT and multiscale permutation entropy features.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Autism spectrum disorder (ASD), a neurodevelopment disorder, is characterized by significant difficulties in social interaction and emerges as a major threat to children. Its computer-aided diagnosis used by neurologists improves the detection proces...

Iterative Reconstruction: State-of-the-Art and Future Perspectives.

Journal of computer assisted tomography
Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented vari...

Children's views on artificial intelligence and digital twins for the daily management of their asthma: a mixed-method study.

European journal of pediatrics
New technologies enable the creation of digital twin systems (DTS) combining continuous data collection from children's home and artificial intelligence (AI)-based recommendations to adapt their care in real time. The objective was to assess whether ...

Artificial Intelligence in Pediatric Nephrology-A Call for Action.

Advances in kidney disease and health
Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused nephrology, artificial intelligence is beginning to be used to improve clinical ...

Efficacy of robot-assisted hepaticojejunostomy and laparoscopic-assisted hepaticojejunostomy in pediatric congenital choledochal dilatation: a system review and meta-analysis.

Pediatric surgery international
PURPOSE: The efficacy of robot-assisted hepaticojejunostomy (RAHJ) and laparoscopic-assisted hepaticojejunostomy (LAHJ) in children with congenital choledochal dilatation has been a topic of much debate and controversy. The purpose of this study was ...

Deep-learning-based personalized prediction of absolute neutrophil count recovery and comparison with clinicians for validation.

Journal of biomedical informatics
Neutropenia and its complications are major adverse effects of cytotoxic chemotherapy. The time to recovery from neutropenia varies from patient to patient, and cannot be easily predicted even by experts. Therefore, we trained a deep learning model u...

A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: The treatment and care of adults and children with traumatic brain injury (TBI) constitute an intractable global health problem. Predicting the prognosis and length of hospital stay of patients with TBI may improve therapeutic effects and...

Characterizing physiological high-frequency oscillations using deep learning.

Journal of neural engineering
Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation...

Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs.

Radiology
Background Supine chest radiography for bedridden patients in intensive care units (ICUs) is one of the most frequently ordered imaging studies worldwide. Purpose To evaluate the diagnostic performance of a neural network-based model that is trained ...