AIMC Topic: Infant, Newborn

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Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations.

Nature medicine
Artificial intelligence (AI) systems have increasingly achieved expert-level performance in medical imaging applications. However, there is growing concern that such AI systems may reflect and amplify human bias, and reduce the quality of their perfo...

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.

Scientific reports
In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of r...

Newborn Eye Screening as an Application of AI.

Ophthalmic surgery, lasers & imaging retina
Artificial intelligence (AI) applications are diverse and serve varied functions in clinical practice. The most successful products today are clinical decision tools used by physicians, but autonomous AI is gaining traction. Widespread use of AI is l...

External validation of a commercially available deep learning algorithm for fracture detection in children.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to conduct an external validation of a fracture assessment deep learning algorithm (Rayvolve®) using digital radiographs from a real-life cohort of children presenting routinely to the emergency room.

Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth.

Scientific reports
Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Wome...

Conversions in pediatric robot-assisted laparoscopic surgery.

Journal of pediatric surgery
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...

Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort.

Scientific reports
Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample popul...

Simulation and beyond - Principles of effective obstetric training.

Best practice & research. Clinical obstetrics & gynaecology
Simulation training provides a safe, non-judgmental environment where members of the multi-professional team can practice both their technical and non-technical skills. Poor teamwork and communication are recurring contributing factors to adverse mat...

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.

PloS one
INTRODUCTION: Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) ...

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

European journal of pediatrics
There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed to identify distinct phenotypes in extremely low birth weight infants. We performed an agglomerativ...