Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications
Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding di...
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| Published in | Frontiers in endocrinology (Lausanne) Vol. 14; p. 1130139 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Media S.A
19.05.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1664-2392 1664-2392 |
| DOI | 10.3389/fendo.2023.1130139 |
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| Abstract | Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.
To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.
Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.
ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.
To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.
The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology. |
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| AbstractList | Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.
To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.
Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.
ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.
To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.
The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology. IntroductionMachine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.AimTo describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.MethodologyPublications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.Current stateML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.Future challengesTo improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.ConclusionThe use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology. Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.IntroductionMachine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.AimTo describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.MethodologyPublications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.Current stateML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.Future challengesTo improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.ConclusionThe use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology. |
| Author | Castro, Erica Eriz-Salinas, Alma Appel-Rubio, Javiera Opazo, Ma. Cecilia Riedel, Claudia A. Aguayo, Claudio Rodríguez, Andrés Mennickent, Daniela Guzmán-Gutiérrez, Enrique Damiano, Alicia E. Araya, Juan |
| AuthorAffiliation | 9 Departamento de Obstetricia y Puericultura, Facultad de Medicina, Universidad de Concepción , Concepción , Chile 3 Machine Learning Applied in Biomedicine (MLAB) , Concepción , Chile 6 Millennium Institute on Immunology and Immunotherapy , Santiago , Chile 11 Laboratorio de Biología de la Reproducción, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO-Houssay)- CONICET, Universidad de Buenos Aires , Buenos Aires , Argentina 1 Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción , Concepción , Chile 8 Departamento de Obstetricia y Puericultura, Facultad de Ciencias de la Salud, Universidad de Atacama , Copiapó , Chile 2 Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción , Concepción , Chile 4 Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío , Chillán , Chile 7 Departamento de Ciencias Biológicas, Facultad de Ciencias de la Vida, Universidad Andrés Bello , Santiago , Chile |
| AuthorAffiliation_xml | – name: 1 Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción , Concepción , Chile – name: 5 Instituto de Ciencias Naturales, Facultad de Medicina Veterinaria y Agronomía, Universidad de Las Américas , Santiago , Chile – name: 7 Departamento de Ciencias Biológicas, Facultad de Ciencias de la Vida, Universidad Andrés Bello , Santiago , Chile – name: 2 Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción , Concepción , Chile – name: 3 Machine Learning Applied in Biomedicine (MLAB) , Concepción , Chile – name: 4 Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío , Chillán , Chile – name: 10 Cátedra de Biología Celular y Molecular, Departamento de Ciencias Biológicas, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires , Buenos Aires , Argentina – name: 6 Millennium Institute on Immunology and Immunotherapy , Santiago , Chile – name: 11 Laboratorio de Biología de la Reproducción, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO-Houssay)- CONICET, Universidad de Buenos Aires , Buenos Aires , Argentina – name: 9 Departamento de Obstetricia y Puericultura, Facultad de Medicina, Universidad de Concepción , Concepción , Chile – name: 8 Departamento de Obstetricia y Puericultura, Facultad de Ciencias de la Salud, Universidad de Atacama , Copiapó , Chile |
| Author_xml | – sequence: 1 givenname: Daniela surname: Mennickent fullname: Mennickent, Daniela – sequence: 2 givenname: Andrés surname: Rodríguez fullname: Rodríguez, Andrés – sequence: 3 givenname: Ma. Cecilia surname: Opazo fullname: Opazo, Ma. Cecilia – sequence: 4 givenname: Claudia A. surname: Riedel fullname: Riedel, Claudia A. – sequence: 5 givenname: Erica surname: Castro fullname: Castro, Erica – sequence: 6 givenname: Alma surname: Eriz-Salinas fullname: Eriz-Salinas, Alma – sequence: 7 givenname: Javiera surname: Appel-Rubio fullname: Appel-Rubio, Javiera – sequence: 8 givenname: Claudio surname: Aguayo fullname: Aguayo, Claudio – sequence: 9 givenname: Alicia E. surname: Damiano fullname: Damiano, Alicia E. – sequence: 10 givenname: Enrique surname: Guzmán-Gutiérrez fullname: Guzmán-Gutiérrez, Enrique – sequence: 11 givenname: Juan surname: Araya fullname: Araya, Juan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37274341$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2023 Mennickent, Rodríguez, Opazo, Riedel, Castro, Eriz-Salinas, Appel-Rubio, Aguayo, Damiano, Guzmán-Gutiérrez and Araya. Copyright © 2023 Mennickent, Rodríguez, Opazo, Riedel, Castro, Eriz-Salinas, Appel-Rubio, Aguayo, Damiano, Guzmán-Gutiérrez and Araya 2023 Mennickent, Rodríguez, Opazo, Riedel, Castro, Eriz-Salinas, Appel-Rubio, Aguayo, Damiano, Guzmán-Gutiérrez and Araya |
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| Keywords | pregnancy complications adverse perinatal outcomes machine learning pregnancy diseases artificial intelligence |
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| SubjectTerms | adverse perinatal outcomes artificial intelligence Endocrinology machine learning pregnancy complications pregnancy diseases |
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| Title | Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications |
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