Pediatrics

Enhancing Fetal Cardiac Imaging With Artificial Intelligence: A Review of the Current Evidence and Future Directions.

Congenital heart disease (CHD) is the most common major birth anomaly and a key cause of neonatal mortality. While early diagnosis improves outcomes, prenatal detection remains inconsistent. Artificial intelligence (AI) offers scalable solutions through automation of view acquisition, image interpretation, and functional assessment. AI has shown expert-level performance in view classification, CHD...

Machine learning-based prediction of large-for-gestational-age neonates in diabetic and non-diabetic pregnancies.

OBJECTIVE: This study determines whether a machine-learning model integrating sonographic biometry with maternal clinical parameters improves prediction of large-for-gestational-age (LGA) compared with Hadlock's EFW formula. METHODS: We conducted a retrospective cohort study including all singleton live births at ≥32 gestational weeks at a tertiary medical center. Predictors comprised biparietal d...

Relationship between cardiometabolic index and worsening renal function in T2DM: Insights from machine learning, growth mixture modelling, and development of a web-based risk prediction tool.

AIM: Worsening renal function (WRF) is a common and serious complication of type 2 diabetes mellitus (T2DM), contributing to adverse clinical outcomes...

A clinical-AI correlation for integrating artificial intelligence into stroke care: a systematized literature review and practice framework.

BACKGROUND AND PURPOSE: The rapid integration of artificial intelligence (AI) into stroke care has outpaced many clinicians' ability to critically eva...

The systemic roots of Chinese nurse burnout: A mixed-methods study using natural language processing of social media data.

BACKGROUND: Burnout, a global occupational health challenge, is particularly prevalent among Chinese nurses. Traditional research methods have limitat...

Artificial Intelligence in Colorectal Cancer Supportive Care: A Scoping Review.

PURPOSE: Artificial Intelligence (AI) has the potential to enhance supportive care for cancer survivors from diagnosis through treatment and into surv...

Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets.

Brain age is an emerging concept that reflects complex, time-dependent changes in brain structure, identifying departures from expected neurodevelopme...

Increased Utilization of Telemedical Emergency and Nonurgent Care Following Deployment of Virtual Triage and Care Referral in Australia.

OBJECTIVE: To evaluate whether an artificial intelligence-based national virtual triage and care referral (VTCR) service in Australia improved care ac...

Disease and state dependent neural markers in adolescents with BD. Understanding the neural bases of mania, depression and remission in a data fusion approach.

BACKGROUND: Pediatric bipolar disorder (PBD) is a severe and disabling condition marked by alternating episodes of mania and depression, intermitted w...

Machine learning analysis of clinical, psychological and sociodemographic factors predicting mental well-being in patients with severe mental illness: Insights from the French REHABase cohort.

PURPOSE: Mental well-being is a cornerstone of recovery for people with mental disorders. Unfortunately, despite many studies on the topic, the litera...

Advancing mold identification in the routine laboratory, performance of smartphone-based imaging and a newly developed convolutional neural network.

Mold identification in clinical diagnostics is traditionally labor-intensive and is dependent on expert interpretation. MoldVision is a deep-learning ...

Development and Validation of an Automated Pipeline for the Detection of Monteggia Fracture Dislocations in Pediatric Radiographs.

BACKGROUND: Monteggia fractures are a complex elbow injury that can be missed in up to 50% of pediatric elbow injuries during initial radiographic ass...

Artificial Intelligence in Pediatric Oncology Care: Pediatric Nurses' Perspectives and Future Implications.

OBJECTIVES: This study aimed to explore pediatric oncology nurses' perspectives on the integration of artificial intelligence (AI) into pediatric onco...

A subject-based association network defines new pediatric sleep apnea phenotypes with different odds of recovery after treatment.

BACKGROUND AND OBJECTIVES: Timely treatment of pediatric obstructive sleep apnea (OSA) can prevent or reverse neurocognitive and cardiovascular morbid...

Diagnostic modalities in pediatric obstructive sleep apnea: A review article.

BACKGROUND: Pediatric obstructive sleep apnea (OSA) affects ∼3 % of children and causes adverse neurocognitive, behavioral, and cardiovascular outcome...

Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.

PURPOSE: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming because of the...

A Learning Accelerator Framework: Scalable Clinical Artificial Intelligence Development and Delivery.

OBJECTIVES: To introduce a vertically integrated model between a health care service provider and technology developer as a learning accelerator to ad...

Self-Driving Development of Perfusion Processes for Monoclonal Antibody Production.

The development of autonomous agents in bioprocess development is crucial for advancing biopharma innovation. Time and resources required to develop a...

Artificial Intelligence in Pediatric Surgery-From Vision to Clinical Reality.

Computer-vision based recognition of cervical spine stabilization during trauma resuscitation.

BACKGROUND: Cervical spine (c-spine) injuries can lead to significant disability and mortality. Although stabilization is the primary management for s...