Pediatrics

Denoising and artefact removal for transthoracic echocardiographic imaging in congenital heart disease: utility of diagnosis specific deep learning algorithms.

Deep learning (DL) algorithms are increasingly used in cardiac imaging. We aimed to investigate the utility of DL algorithms in de-noising transthoracic echocardiographic images and removing acoustic shadowing artefacts specifically in patients with congenital heart disease (CHD). In addition, the performance of DL algorithms trained on CHD samples was compared to models trained entirely on struct...

Children's Fear and Pain During Medical Procedures: A Quality Improvement Study With a Humanoid Robot.

BACKGROUND: Pediatric patients undergo a variety of painful medical procedures.

Sentiment Analysis of the News Media on Artificial Intelligence Does Not Support Claims of Negative Bias Against Artificial Intelligence.

Artificial intelligence (AI) is a hot topic in digital health, as automated systems are being adopted throughout the health care system. Because they ...

Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm.

Accounting for the morphology of shale formations, which represent highly heterogeneous porous media, is a difficult problem. Although two- or three-d...

Superhuman AI for multiplayer poker.

In recent years there have been great strides in artificial intelligence (AI), with games often serving as challenge problems, benchmarks, and milesto...

Demographic, psychological, behavioral, and cognitive correlates of BMI in youth: Findings from the Adolescent Brain Cognitive Development (ABCD) study.

BACKGROUND: Previous research has implicated demographic, psychological, behavioral, and cognitive variables in the onset and maintenance of pediatric...

Automated Flow Cytometric MRD Assessment in Childhood Acute B- Lymphoblastic Leukemia Using Supervised Machine Learning.

Minimal residual disease (MRD) as measured by multiparameter flow cytometry (FCM) is an independent and strong prognostic factor in B-cell acute lymph...

Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions.

IMPORTANCE: Early palliative care interventions drive high-value care but currently are underused. Health care professionals face challenges in identi...

Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT).

PURPOSE: Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with...

Robotic Sensing and Stimuli Provision for Guided Plant Growth.

Robot systems are actively researched for manipulation of natural plants, typically restricted to agricultural automation activities such as harvest, ...

Using machine learning to characterize heart failure across the scales.

Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in ...

Object Detection During Newborn Resuscitation Activities.

OBJECTIVE: Birth asphyxia is a major newborn mortality problem in low-resource countries. International guideline provides treatment recommendations; ...

A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm.

Imaging studies have characterized functional and structural brain abnormalities in adults after premature birth, but these investigations have mostly...

Role of deep learning in infant brain MRI analysis.

Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relativ...

Effects of different moisture and temperature levels on survival in poultry fat.

Fat products have been historically thought to have too low water activity to harbor pathogens. However, it has been recently reported that high moist...

A Machine Learning Approach for the Detection and Characterization of Illicit Drug Dealers on Instagram: Model Evaluation Study.

BACKGROUND: Social media use is now ubiquitous, but the growth in social media communications has also made it a convenient digital platform for drug ...

SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses.

Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variab...

Regenerative robotics.

Congenital diseases requiring reconstruction of parts of the gastrointestinal tract, skin, or bone are a challenge to alleviate especially in rapidly ...

Deep learning-based carotid media-adventitia and lumen-intima boundary segmentation from three-dimensional ultrasound images.

PURPOSE: Quantification of carotid plaques has been shown to be important for assessing as well as monitoring the progression and regression of caroti...

Using Machine Learning to Identify Suicide Risk: A Classification Tree Approach to Prospectively Identify Adolescent Suicide Attempters.

This study applies classification tree analysis to prospectively identify suicide attempters among a large adolescent community sample, to demonstrate...