Predicting Preterm Birth Using Multimodal Fetal Imaging
Heinsalu R, Williams L, Ranjan A, Zampieri CA, Uus A, Robinson EC, Rutherford MA, Story L, Hutter J (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12959 LNCS
Pages Range: 284-293
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030877347
DOI: 10.1007/978-3-030-87735-4_27
Abstract
Preterm birth (PTB) (<37 weeks’ gestational age (GA)) is associated with increased risk of short- and long-term sequelae. Accurate predictive tools allow to improve the outcomes of those born preterm by offering early obstetric interventions to mothers at high-risk of PTB. Methods: This study combines a wide range of structural and functional MRI parameters, from the fetal head, lung, placenta with clinically available Ultrasound and outcome data. A preprocessing pipeline adapted to the special requirements of the often incomplete and highly GA dependant data and a supervised machine learning model based on these derived markers derived is proposed. Data from 58 preterm and 217 term-born neonates were analysed. Results: The best SVR model achieved an R2 value of 0.67 and correctly predicted 92% of true preterm cases using a combination of two maternal and four fetal features. Conclusion: The significance of this study is uncovering the potential of markers derived from multi-modal imaging data in the prediction of PTB using large-scale fetal studies. This study paves the way for future studies focusing on at-risk women to further enhance the data set and thus predictive power.
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United Kingdom (GB)How to cite
APA:
Heinsalu, R., Williams, L., Ranjan, A., Zampieri, C.A., Uus, A., Robinson, E.C.,... Hutter, J. (2021). Predicting Preterm Birth Using Multimodal Fetal Imaging. In Carole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, Christopher Macgowan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 284-293). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Heinsalu, Riine, et al. "Predicting Preterm Birth Using Multimodal Fetal Imaging." Proceedings of the 3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Carole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, Christopher Macgowan, Springer Science and Business Media Deutschland GmbH, 2021. 284-293.
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