Pediatrics

Distinguishing neonatal culture-negative sepsis from rule-out sepsis with artificial intelligence-derived graphs.

Novel artificial intelligence methods can aide in identification of cases of conditions using only unstructured electronic health record data. This graph-based method compares comprehensive electronic health records among neonates using temporal data. This provides a scalable solution to distinguish culture negative sepsis from rule out sepsis using a data-driven method.

Artificial intelligence and myocarditis-a systematic review of current applications.

Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study, guided by PRISMA guidelines, explores the expanding role of artificial intelligence (AI) in myocarditis, aiming to consolidate current knowledge and guide future research. Following PRISMA guidelines, a systematic review was conducted across PubMed, Cochrane Reviews, Scopus, Embase, and Web of Scie...

Using machine learning to explore the predictors of life satisfaction trajectories in older adults.

Life satisfaction is vital for older adults' well-being, impacting various life aspects. It is dynamic, necessitating nuanced approaches to capture it...

Predicting ultrasound wave stimulated bone growth in bioinspired scaffolds using machine learning.

For conditions like osteoporosis, changes in bone pore geometry even when porosity is constant have been shown to correlate to increased fracture risk...

Predicting the trajectory of non-suicidal self-injury among adolescents.

BACKGROUND: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a h...

Demystifying Artificial Intelligence for Health Care Professionals: Continuing Professional Development as an Agent of Transformation Leading to Artificial Intelligence-Augmented Practice.

The rapid rise of artificial intelligence (AI) is transforming society; yet, the education of health care providers in this field is lagging. In healt...

Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence.

BACKGROUND: Prevalence of suicidal behaviour increases rapidly in middle to late adolescence. Predicting suicide attempts across different ages would ...

A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.

We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones ...

Capability of multimodal large language models to interpret pediatric radiological images.

BACKGROUND: There is a dearth of artificial intelligence (AI) development and research dedicated to pediatric radiology. The newest iterations of larg...

Artificial intelligence reveals the predictions of hematological indexes in children with acute leukemia.

Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary...

Being diagnosed with a rhabdomyosarcoma in the era of artificial intelligence: Whom can we trust?

In the era of big data, young patients may be overwhelmed by artificial intelligence-based tools, like chatbots. Five clinical experts were asked to e...

Novel use of deep neural networks on photographic identification of epaulette sharks (Hemiscyllium ocellatum) across life stages.

Photographic identification (photo ID) is an established method that is used to count animals and track individuals' movements. This method performs w...

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain t...

Morphological Rule-Constrained Object Detection of Key Structures in Infant Fundus Image.

The detection of optic disc and macula is an essential step for ROP (Retinopathy of prematurity) zone segmentation and disease diagnosis. This paper a...

SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects.

Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for indiv...

Targeting Machine Learning and Artificial Intelligence Algorithms in Health Care to Reduce Bias and Improve Population Health.

Policy Points Artificial intelligence (AI) is disruptively innovating health care and surpassing our ability to define its boundaries and roles in hea...

A 3D and Explainable Artificial Intelligence Model for Evaluation of Chronic Otitis Media Based on Temporal Bone Computed Tomography: Model Development, Validation, and Clinical Application.

BACKGROUND: Temporal bone computed tomography (CT) helps diagnose chronic otitis media (COM). However, its interpretation requires training and expert...

The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.

Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-iden...

Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks.

Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data pri...

A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images.

Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key ...