Pediatrics

Predicting Post-Concussion Symptom Recovery in Adolescents Using a Novel Artificial Intelligence.

This pilot study explores the possibility of predicting post-concussion symptom recovery at one week post-injury using only objective diffusion tensor imaging (DTI) data inputs to a novel artificial intelligence (AI) system composed of Genetic Fuzzy Trees (GFT). Forty-three adolescents age 11 to 16 years with either mild traumatic brain injury or traumatic orthopedic injury were enrolled on presen...

Machine learning approach for predicting Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with ethylene-vinyl alcohol copolymer films containing pure components of essential oils.

Fusarium culmorum and F. proliferatum can grow and produce, respectively, zearalenone (ZEA) and fumonisins (FUM) in different points of the food chain. Application of antifungal chemicals to control these fungi and mycotoxins increases the risk of toxic residues in foods and feeds, and induces fungal resistances. In this study, a new and multidisciplinary approach based on the use of bioactive eth...

Evaluation of the Artificial Neural Network and Naive Bayes Models Trained with Vertebra Ratios for Growth and Development Determination.

OBJECTIVE: This study aimed to evaluate the success rates of the artificial neural network models (NNMs) and naive Bayes models (NBMs) trained with va...

Evolution of Wearable Devices in Health Coaching: Challenges and Opportunities.

Wearable devices hold an enormous potential in contributing to an improved global health. The availability, non-invasiveness, and affordability of tho...

Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.

OBJECTIVE: Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative m...

Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases.

BACKGROUND: For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency...

Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment.

The current practice for assessing neonatal postoperative pain relies on bedside caregivers. This practice is subjective, inconsistent, slow, and disc...

Training confounder-free deep learning models for medical applications.

The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging ...

Deep learning enabled prediction of 5-year survival in pediatric genitourinary rhabdomyosarcoma.

BACKGROUND: Genitourinary rhabdomyosarcoma (GU-RMS) is a rare, pediatric malignancy originating from embryonic mesenchyme. Current approaches to progn...

Automatic eye localization for hospitalized infants and children using convolutional neural networks.

BACKGROUND: Reliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decisio...

On the use of AI for Generation of Functional Music to Improve Mental Health.

Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reductio...

Identification of prognostic factors for pediatric myocarditis with a random forests algorithm-assisted approach.

BACKGROUND: Pediatric myocarditis is a rare disease with substantial mortality. Little is known regarding its prognostic factors. We hypothesize that ...

Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction.

Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation...

Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments.

BACKGROUND: Objectives were to build a machine learning algorithm to identify bloodstream infection (BSI) among pediatric patients with cancer and hem...

Reliable or not? An automated classification of webpages about early childhood vaccination using supervised machine learning.

OBJECTIVE: To investigate the applicability of supervised machine learning (SML) to classify health-related webpages as 'reliable' or 'unreliable' in ...

An Argument for an Ecosystemic AI: Articulating Connections across Prehuman and Posthuman Intelligences.

As an art collective Cesar & Lois develops projects that examine sociotechnical systems, attempting to challenge anthropocentric technological pathway...

The role of artificial intelligence in cosmetic dermatology-Current, upcoming, and future trends.

Within the field of cosmetic dermatology, several promising developments utilize artificial intelligence to better patient care. While many new treatm...

Metamorphosis in Insect Muscle: Insights for Engineering Muscle-Based Actuators.

One of the major limitations to advancing the development of soft robots is the absence of lightweight, effective soft actuators. While synthetic syst...

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae.

Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and manageme...

Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

OBJECTIVE: In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the P...