Machine Learning Methods for Preterm Birth Prediction: A Review
Preterm births affect around 15 million children a year worldwide. Current medical efforts focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are based on parent traits and transvaginal ultrasound, during which the length of the cervix is examined. Approximately...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/5/586 |
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| author | Tomasz Włodarczyk Szymon Płotka Tomasz Szczepański Przemysław Rokita Nicole Sochacki-Wójcicka Jakub Wójcicki Michał Lipa Tomasz Trzciński |
| author_facet | Tomasz Włodarczyk Szymon Płotka Tomasz Szczepański Przemysław Rokita Nicole Sochacki-Wójcicka Jakub Wójcicki Michał Lipa Tomasz Trzciński |
| author_sort | Tomasz Włodarczyk |
| collection | DOAJ |
| description | Preterm births affect around 15 million children a year worldwide. Current medical efforts focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are based on parent traits and transvaginal ultrasound, during which the length of the cervix is examined. Approximately 30% of preterm births are not correctly predicted due to the complexity of this process and its subjective assessment. Based on recent research, there is hope that machine learning can be a helpful tool to support the diagnosis of preterm births. The objective of this study is to present various machine learning algorithms applied to preterm birth prediction. The wide spectrum of analysed data sets is the advantage of this survey. They range from electrohysterogram signals through electronic health records to transvaginal ultrasounds. Reviews of works on preterm birth already exist; however, this is the first review that includes works that are based on a transvaginal ultrasound examination. In this work, we present a critical appraisal of popular methods that have employed machine learning methods for preterm birth prediction. Moreover, we summarise the most common challenges incurred and discuss their possible application in the future. |
| first_indexed | 2024-03-09T05:51:10Z |
| format | Article |
| id | doaj.art-40d0cd7fe240444a90fd6efcd5c4c7d8 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T05:51:10Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-40d0cd7fe240444a90fd6efcd5c4c7d82023-12-03T12:17:24ZengMDPI AGElectronics2079-92922021-03-0110558610.3390/electronics10050586Machine Learning Methods for Preterm Birth Prediction: A ReviewTomasz Włodarczyk0Szymon Płotka1Tomasz Szczepański2Przemysław Rokita3Nicole Sochacki-Wójcicka4Jakub Wójcicki5Michał Lipa6Tomasz Trzciński7Institute of Computer Science, Warsaw University of Technology, 00-661 Warsaw, PolandInstitute of Computer Science, Warsaw University of Technology, 00-661 Warsaw, PolandInstitute of Computer Science, Warsaw University of Technology, 00-661 Warsaw, PolandInstitute of Computer Science, Warsaw University of Technology, 00-661 Warsaw, Poland1st Department of Obstetrics and Gynecology, Medical University of Warsaw, 02-091 Warsaw, Poland1st Department of Obstetrics and Gynecology, Medical University of Warsaw, 02-091 Warsaw, Poland1st Department of Obstetrics and Gynecology, Medical University of Warsaw, 02-091 Warsaw, PolandInstitute of Computer Science, Warsaw University of Technology, 00-661 Warsaw, PolandPreterm births affect around 15 million children a year worldwide. Current medical efforts focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are based on parent traits and transvaginal ultrasound, during which the length of the cervix is examined. Approximately 30% of preterm births are not correctly predicted due to the complexity of this process and its subjective assessment. Based on recent research, there is hope that machine learning can be a helpful tool to support the diagnosis of preterm births. The objective of this study is to present various machine learning algorithms applied to preterm birth prediction. The wide spectrum of analysed data sets is the advantage of this survey. They range from electrohysterogram signals through electronic health records to transvaginal ultrasounds. Reviews of works on preterm birth already exist; however, this is the first review that includes works that are based on a transvaginal ultrasound examination. In this work, we present a critical appraisal of popular methods that have employed machine learning methods for preterm birth prediction. Moreover, we summarise the most common challenges incurred and discuss their possible application in the future.https://www.mdpi.com/2079-9292/10/5/586artificial intelligencedeep learningmachine learningpreterm birth |
| spellingShingle | Tomasz Włodarczyk Szymon Płotka Tomasz Szczepański Przemysław Rokita Nicole Sochacki-Wójcicka Jakub Wójcicki Michał Lipa Tomasz Trzciński Machine Learning Methods for Preterm Birth Prediction: A Review Electronics artificial intelligence deep learning machine learning preterm birth |
| title | Machine Learning Methods for Preterm Birth Prediction: A Review |
| title_full | Machine Learning Methods for Preterm Birth Prediction: A Review |
| title_fullStr | Machine Learning Methods for Preterm Birth Prediction: A Review |
| title_full_unstemmed | Machine Learning Methods for Preterm Birth Prediction: A Review |
| title_short | Machine Learning Methods for Preterm Birth Prediction: A Review |
| title_sort | machine learning methods for preterm birth prediction a review |
| topic | artificial intelligence deep learning machine learning preterm birth |
| url | https://www.mdpi.com/2079-9292/10/5/586 |
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