Latest AI and machine learning research in pediatrics for healthcare professionals.
PURPOSE: Decision-making for orchiectomy following testicular torsion often relies on subjective clinical evaluations. This study investigates the efficacy of machine learning (ML) models in objectively predicting post-torsion testicular viability, aiming to maintain a parenchymal ratio over 80% compared to the contralateral testicle, irrespective of initial appearance and surgical timing. METHODS...
UNLABELLED: Childhood obesity is the main driver of early metabolic risk, predisposing to cardiovascular disease (CVD) and type 2 diabetes (T2D), which cause millions of deaths worldwide. Their progression is influenced by biological, behavioral, and environmental factors. Digital Twin Systems (DTS) offer innovative ways to monitor and predict cardiometabolic risk. This work presents a prototype d...
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into healthcare, offering potential advancements in pat...
BACKGROUND: Language barriers in pediatric emergency medicine discharge instructions can impact patient safety, leading to poorer post-discharge outco...
IMPORTANCE: The potential of tools using artificial intelligence (AI) to address the many challenges in delivery of mental health care has been widely...
BACKGROUND: Artificial intelligence (AI) offers potential solutions to address the challenges faced by a strained mental health care system, such as i...
PURPOSE: There are no specific guidelines for posterior cranial fossa decompression (PCFD) in asymptomatic Chiari Malformation Type I (CM-I) patients ...
Pediatric obesity is linked to multi-organ inflammation and an increased risk of cardiometabolic and steatotic liver disease. To identify circulating ...
BACKGROUND: Advances in artificial intelligence (AI) have revolutionized digital wellness by providing innovative solutions for health, social connect...
OBJECTIVE: This study addresses these challenges by developing a robust and efficient deep learning model for automated segmentation using a large and...
Acute ischemic stroke (AIS) outcomes depend critically on rapid, accurate early diagnosis in the emergency department. Traditional prehospital tools a...
OBJECTIVES: Plain abdominal radiography is a widely used imaging modality for diagnosing neonatal necrotizing enterocolitis (NEC), but the characteris...
OBJECTIVES: To develop and validate a deep learning (DL)-based algorithm for automated measurement of femoral head ossification center (FHOC) size and...
Early and precise diagnosis of gestational diabetes mellitus (GDM) is crucial for improving maternal and neonatal outcomes and reducing the risk of ad...
OBJECTIVES: To assess the concordance and diagnosis acceptability of differential diagnoses (DDs) generated by artificial intelligence (AI) chatbots c...
PURPOSE OF REVIEW: Perinatal depression (PND) affects up to one in five patients and is the leading cause of maternal mortality, yet remains underdiag...
INTRODUCTION: This study was conducted to explore the views of pediatric nurses on robot nurses and artificial intelligence. METHOD: A qualitative res...
BACKGROUND: Podcasts are a popular educational tool in health professions that appeal to learners for their accessibility and flexibility. Skeptics cr...
Artificial intelligence (AI) is rapidly transforming pediatric healthcare, yet many pediatric nurse practitioners lack adequate preparation for implem...