AIMC Topic: Intensive Care Units, Neonatal

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From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit.

Intensive care medicine
PURPOSE: Despite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benef...

Comparative analysis of artificial intelligence and expert assessments in detecting neonatal procedural pain.

Scientific reports
Assessing pain in newborns in the NICU is crucial due to their frequent exposure to painful stimuli, yet it's challenging due to the subjective nature of current methods. This study aimed to evaluate the effectiveness of an AI system designed for aut...

Evaluation of Comfort Behavior Levels of Newborn by Artificial Intelligence Techniques.

The Journal of perinatal & neonatal nursing
BACKGROUND: One of the scales most frequently used in the evaluation of newborn comfort levels is the Neonatal Comfort Behavior Scale (NCBS). It is important therefore that an increased use of the NCBS is encouraged through a more practical method of...

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.

Frontiers in public health
INTRODUCTION: Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-te...

Performance Evaluation of a Supervised Machine Learning Pain Classification Model Developed by Neonatal Nurses.

Advances in neonatal care : official journal of the National Association of Neonatal Nurses
BACKGROUND: Early-life pain is associated with adverse neurodevelopmental consequences; and current pain assessment practices are discontinuous, inconsistent, and highly dependent on nurses' availability. Furthermore, facial expressions in commonly u...

Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.

Pediatric research
BACKGROUND: Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hind...

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...

Artificial intelligence in the neonatal intensive care unit: the time is now.

Journal of perinatology : official journal of the California Perinatal Association
Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimensional data that are generated by NICU patients. There is an emerging recognition that the confluenc...

Face-based automatic pain assessment: challenges and perspectives in neonatal intensive care units.

Jornal de pediatria
OBJECTIVE: To describe the challenges and perspectives of the automation of pain assessment in the Neonatal Intensive Care Unit.

Ensemble Approach on Deep and Handcrafted Features for Neonatal Bowel Sound Detection.

IEEE journal of biomedical and health informatics
For the care of neonatal infants, abdominal auscultation is considered a safe, convenient, and inexpensive method to monitor bowel conditions. With the help of early automated detection of bowel dysfunction, neonatologists could create a diagnosis pl...