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Infant, Premature

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A Deep-Learning-Based Collaborative Edge-Cloud Telemedicine System for Retinopathy of Prematurity.

Sensors (Basel, Switzerland)
Retinopathy of prematurity is an ophthalmic disease with a very high blindness rate. With its increasing incidence year by year, its timely diagnosis and treatment are of great significance. Due to the lack of timely and effective fundus screening fo...

Image Reconstruction Using Deep Learning for Near-Infrared Optical Tomography: Generalization Assessment.

Advances in experimental medicine and biology
Time is one of the most critical factors in preventing brain lesions due to hypoxic ischemia in preterm infants. Since early detection of low oxygenation is vital and the time window for therapy is narrow, near-infrared optical tomography (NIROT) mus...

Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the Literature.

Neonatology
Retinopathy of prematurity (ROP) is a potentially blinding disease in premature neonates that requires a skilled workforce for diagnosis, monitoring, and treatment. Artificial intelligence is a valuable tool that clinicians employ to reduce the scree...

Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study.

The Lancet. Digital health
BACKGROUND: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmologists. However, improved survival of premature neonates coupled with a scarcity of available expert...

A deep sift convolutional neural networks for total brain volume estimation from 3D ultrasound images.

Computer methods and programs in biomedicine
Preterm infants are a highly vulnerable population. The total brain volume (TBV) of these infants can be accurately estimated by brain ultrasound (US) imaging which enables a longitudinal study of early brain growth during Neonatal Intensive Care (NI...

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

European radiology
OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphom...

Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm.

Physical therapy
OBJECTIVE: Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whethe...