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Bertini A, Salas R, Chabert S, Sobrevia L, Pardo F. Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review. Front Bioeng Biotechnol 2022; 9:780389. [PMID: 35127665 PMCID: PMC8807522 DOI: 10.3389/fbioe.2021.780389] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/10/2021] [Indexed: 12/11/2022] Open
Abstract
Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications.Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications.Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method.Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy.Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.
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Affiliation(s)
- Ayleen Bertini
- Metabolic Diseases Research Laboratory (MDRL), Interdisciplinary Center for Research in Territorial Health of the Aconcagua Valley (CIISTe Aconcagua), Center for Biomedical Research (CIB), Universidad de Valparaíso, Valparaiso, Chile
- PhD Program Doctorado en Ciencias e Ingeniería para La Salud, Faculty of Medicine, Universidad de Valparaíso, Valparaiso, Chile
| | - Rodrigo Salas
- School of Biomedical Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaiso, Chile
- Centro de Investigación y Desarrollo en INGeniería en Salud – CINGS, Universidad de Valparaíso, Valparaiso, Chile
- Instituto Milenio Intelligent Healthcare Engineering, Valparaíso, Chile
| | - Steren Chabert
- School of Biomedical Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaiso, Chile
- Centro de Investigación y Desarrollo en INGeniería en Salud – CINGS, Universidad de Valparaíso, Valparaiso, Chile
- Instituto Milenio Intelligent Healthcare Engineering, Valparaíso, Chile
| | - Luis Sobrevia
- Cellular and Molecular Physiology Laboratory (CMPL), Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Physiology, Faculty of Pharmacy, Universidad de Sevilla, Seville, Spain
- University of Queensland Centre for Clinical Research (UQCCR), Faculty of Medicine and Biomedical Sciences, University of Queensland, Herston, QLD, Australia
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Medical School (Faculty of Medicine), São Paulo State University (UNESP), São Paulo, Brazil
- Tecnologico de Monterrey, Eutra, The Institute for Obesity Research, School of Medicine and Health Sciences, Monterrey, Mexico
| | - Fabián Pardo
- Metabolic Diseases Research Laboratory (MDRL), Interdisciplinary Center for Research in Territorial Health of the Aconcagua Valley (CIISTe Aconcagua), Center for Biomedical Research (CIB), Universidad de Valparaíso, Valparaiso, Chile
- Cellular and Molecular Physiology Laboratory (CMPL), Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- School of Medicine, Campus San Felipe, Faculty of Medicine, Universidad de Valparaíso, San Felipe, Chile
- *Correspondence: Fabián Pardo,
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