PURPOSE: Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artifici...
PURPOSE: To describe a novel modification of technique to improve efficacy of robot-assisted laparoscopic extravesical ureteral reimplantation (RALUR-EV) in infants.
BACKGROUND: New technology attracts necessary concerns regarding safety and effectiveness, including the risk and circumstances of conversions. This study analyses our 11-year experience of conversions from a dedicated pediatric robot-assisted laparo...
PURPOSE: We aimed to introduce an explainable machine learning technology to help clinicians understand the risk factors for neonatal postoperative mortality at different levels.
BACKGROUND: The recognition of child physical abuse can be challenging and often requires a multidisciplinary assessment. Deep learning models, based on clinical characteristics, laboratory studies, and imaging findings, were developed to facilitate ...
BACKGROUND: The principal triggers for intervention in the setting of pediatric blunt solid organ injury (BSOI) are declining hemoglobin values and hemodynamic instability. The clinical management of BSOI is, however, complex. We therefore hypothesiz...
Exponential growth in computing power, data storage, and sensing technology has led to a world in which we can both capture and analyze incredible amounts of data. The evolution of machine learning has further advanced the ability of computers to dev...
Academic medicine is experiencing an exponential increase in knowledge, evidenced by approximately 2.5 million new articles published each year. As a result, staying apprised of practice-changing findings as a busy clinician is nearly impossible. The...