AIMC Topic: Infant, Newborn

Clear Filters Showing 301 to 310 of 815 articles

Predicting low cognitive ability at age 5 years using perinatal data and machine learning.

Pediatric research
BACKGROUND: There are no early, accurate, scalable methods for identifying infants at high risk of poor cognitive outcomes in childhood. We aim to develop an explainable predictive model, using machine learning and population-based cohort data, for t...

A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities.

Scientific data
Early and accurate diagnosis of ear deformities in newborns is crucial for an effective non-surgical correction treatment, since this commonly seen ear anomalies would affect aesthetics and cause mental problems if untreated. It is not easy even for ...

Pregnancy-associated asymptomatic bacteriuria and antibiotic resistance in the Maternity and Children's Hospital, Arar, Saudi Arabia.

Journal of infection in developing countries
INTRODUCTION: The Ministry of Health in Saudi Arabia provides comprehensive antenatal care for all pregnant women with all required investigations. However, it does not include urine culture for diagnosis of asymptomatic bacteriuria (ASB). This is th...

Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women.

PloS one
Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing mo...

Neurologic Statistical Prognostication and Risk Assessment for Kids on Extracorporeal Membrane Oxygenation-Neuro SPARK.

ASAIO journal (American Society for Artificial Internal Organs : 1992)
This study presents Neuro-SPARK, the first scoring system developed to assess the risk of neurologic injury in pediatric and neonatal patients on extracorporeal membrane oxygenation (ECMO). Using the extracorporeal life support organization (ELSO) re...

Pediatric evaluations for deep learning CT denoising.

Medical physics
BACKGROUND: Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL deno...

Assessing the Utility of a Machine-Learning Model to Assist With the Assignment of the American Society of Anesthesiology Physical Status Classification in Pediatric Patients.

Anesthesia and analgesia
BACKGROUND: The American Society of Anesthesiologists Physical Status Classification System (ASA-PS) is used to classify patients' health before delivering an anesthetic. Assigning an ASA-PS Classification score to pediatric patients can be challengi...

Artificial intelligence in the NICU to predict extubation success in prematurely born infants.

Journal of perinatal medicine
OBJECTIVES: Mechanical ventilation in prematurely born infants, particularly if prolonged, can cause long term complications including bronchopulmonary dysplasia. Timely extubation then is essential, yet predicting its success remains challenging. Ar...

Establishment of Biliary Atresia Prognostic Classification System via Survival-Based Forward Clustering - A New Biliary Atresia Classification.

Indian journal of pediatrics
OBJECTIVES: To develop a machine learning algorithm with prognosis data to identify different clinical phenotypes of biliary atresia (BA) and provide instructions for choosing treatment schemes.

A Machine Learning Algorithm using Clinical and Demographic Data for All-Cause Preterm Birth Prediction.

American journal of perinatology
OBJECTIVE: Preterm birth remains the predominant cause of perinatal mortality throughout the United States and the world, with well-documented racial and socioeconomic disparities. To develop and validate a predictive algorithm for all-cause preterm ...