Application of artificial intelligence in screening for adverse perinatal outcomes: A protocol for systematic review.
Stepan FeduniwDorota SysSebastian KwiatkowskiAnna KajdyPublished in: Medicine (2021)
The article presents a systematic review protocol. The aim of the study is an assessment of current studies regarding the application of artificial intelligence and neural networks in the screening for adverse perinatal outcomes. We intend to compare the reported efficacy of these methods to improve pregnancy care and outcomes. There are more and more studies that describe the role of machine learning in facilitating the diagnosis of adverse perinatal outcomes, like gestational diabetes or pregnancy hypertension. A systematic review of available literature seems to be crucial to compare the known efficacy and application. Publication of a systematic review in this category would improve the value of future studies. The studies reporting on artificial intelligence application will have a major impact on future prenatal practice.
Keyphrases
- artificial intelligence
- machine learning
- big data
- deep learning
- pregnant women
- healthcare
- case control
- blood pressure
- systematic review
- neural network
- preterm birth
- type diabetes
- current status
- pregnancy outcomes
- metabolic syndrome
- emergency department
- adverse drug
- skeletal muscle
- insulin resistance
- glycemic control
- electronic health record
- weight loss