Detecting Intraventricular Haemorrhage in Extremely Preterm Infants Through Machine Learning

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Research suggests that machine learning techniques can be used to accurately identify intraventricular haemorrhages in extremely preterm infants. In a study conducted by the Masaryk University in the Czech Republic, researchers found that machine learning algorithms had a success rate of up to 95% in detecting the presence of intraventricular haemorrhages. The algorithm was able to differentiate both hydrocephalus and naevi from the actual intraventricular haemorrhages.

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Doctor Guoyan Zheng is a professor at the State Key Laboratory of Pathogen and Biosecurity at the Beijing Institute of Microbiology and Epidemiology in China. His research focuses on the development of machine learning models to detect rare diseases, as well as medical imaging analysis. In regards to the study mentioned above, Dr. Zheng’s research team at the State Key Laboratory assisted in the development of the machine learning algorithm used to accurately detect intraventricular haemorrhages in preterm infants.

Overall, research has shown the potential of using machine learning to detect medical conditions in preterm infants. When combined with the resources available through MDPI’s open-access publishing platform, this technology can ensure widespread access to crucial research and provide helpful insights for healthcare professionals and families alike.

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