Pediatrics

Robust identification key predictors of short- and long-term weight status in children and adolescents by machine learning.

BACKGROUND: Early identification of high-risk individuals for weight problems in children and adolescents is crucial for implementing timely preventive measures. While machine learning (ML) techniques have shown promise in addressing this complex challenge with high-dimensional data, feature selection is vital for identifying the key predictors that can facilitate effective and targeted interventi...

Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.

Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large...

Radiation dose reduction in pediatric computed tomography (CT) using deep convolutional neural network denoising.

AIM: We evaluated the quality of noncontrast chest computed tomography (CT) for pediatric patients at two dose levels with and without denoising using...

Chatbots in Limb Lengthening and Reconstruction Surgery: How Accurate Are the Responses?

BACKGROUND: Artificial intelligence-based language model chatbots are being increasingly used as a quick reference for healthcare related information....

The Emerging Role of Artificial Intelligence and Automated Platforms for the Assessment of Penile Curvature: A Scoping Review.

PURPOSE OF THE REVIEW: The estimation of penile curvature is an essential component in the assessment of both Peyronie's disease and hypospadias-assoc...

Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches.

To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based co...

An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors.

BACKGROUND: Kidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function...

Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population.

Deep learning (DL) tools developed on adult data sets may not generalize well to pediatric patients, posing potential safety risks. We evaluated the p...

Artificial intelligence in paediatric head trauma: enhancing diagnostic accuracy for skull fractures and brain haemorrhages.

Pediatric head trauma is a significant cause of morbidity and mortality, with children, particularly those under two years old, being more susceptible...

Machine learning-based early prediction of growth and morphological traits at yearling age in pure and hybrid goat offspring.

The purpose of this study was to evaluate the performance of various prediction models in estimating the growth and morphological traits of pure Hair,...

What explains adolescents' physical activity and sports participation during the COVID-19 pandemic? - an interpretable machine learning approach.

Adolescents' physical activity (PA) and sports participation declined due to the COVID-19 pandemic. This study aimed to determine the critical socio-e...

Physics-informed neural networks for biopharmaceutical cultivation processes: Consideration of varying process parameter settings.

We present a new modeling approach for the study and prediction of important process outcomes of biotechnological cultivation processes under the infl...

Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.

BACKGROUND: The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitud...

PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal.

Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethy...

Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data.

BACKGROUND: Newborns are shaped by prenatal maternal experiences. These include a pregnant person's physical health, prior pregnancy experiences, emot...

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control.

Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices...

An automated framework for pediatric hip surveillance and severity assessment using radiographs.

PURPOSE: Hip dysplasia is the second most common orthopedic condition in children with cerebral palsy (CP) and may result in disability and pain. The ...

Machine learning-based discrimination of unipolar depression and bipolar disorder with streamlined shortlist in adolescents of different ages.

BACKGROUND: Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimina...

Nursing Staff's Perspectives of Care Robots for Assisted Living Facilities: Systematic Literature Review.

BACKGROUND: Care robots have been proposed in response to nursing shortages in assisted living facilities (ALFs) and the growing population of older a...

Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study.

BACKGROUND: Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up mana...