首页|Studies from James Cook University Yield New Information about Premature Birth ( Machine Learning for Understanding and Predicting Neurodevelopmental Outcomes In Premature Infants: a Systematic Review)
Studies from James Cook University Yield New Information about Premature Birth ( Machine Learning for Understanding and Predicting Neurodevelopmental Outcomes In Premature Infants: a Systematic Review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Pregnan cy Complications - Premature Birth. According to news reporting originating in C airns, Australia, by NewsRx journalists, research stated, "Machine learning has been attracting increasing attention for use in healthcare applications, includi ng neonatal medicine. One application for this tool is in understanding and pred icting neurodevelopmental outcomes in preterm infants." Financial support for this research came from CAUL. The news reporters obtained a quote from the research from James Cook University, "In this study, we have carried out a systematic review to identify findings a nd challenges to date. This systematic review was conducted in accordance with t he Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines . Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors. The literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural ne tworks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing b rain structure and function. Studies to date also sought to identify features pr edictive of outcomes; however, results varied greatly. Initial studies in this f ield have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes. Impact Th is systematic review looks at the question of whether machine learning can be us ed to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, bu t many challenges and opportunities remain. Quality assessment of relevant artic les is conducted using the Newcastle-Ottawa Scale. This work identifies challeng es that remain and suggests several key directions for future research."
CairnsAustraliaAustralia and New Zea landCyborgsEmerging TechnologiesHealth and MedicineMachine LearningPre gnancy ComplicationsPremature BirthJames Cook University
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