A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system
Objective This study aims to address the limitations of current clinical methods in predicting delivery mode by constructing a multimodal neural network-based model. The model utilizes data from a digital twin-empowered labor monitoring system, including computerized cardiotocography (cCTG), ultraso...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-12-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241304934 |
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| author | Jieyun Bai Xue Kang Weishan Wang Ziduo Yang Weiguang Ou Yuxin Huang Yaosheng Lu |
| author_facet | Jieyun Bai Xue Kang Weishan Wang Ziduo Yang Weiguang Ou Yuxin Huang Yaosheng Lu |
| author_sort | Jieyun Bai |
| collection | DOAJ |
| description | Objective This study aims to address the limitations of current clinical methods in predicting delivery mode by constructing a multimodal neural network-based model. The model utilizes data from a digital twin-empowered labor monitoring system, including computerized cardiotocography (cCTG), ultrasound (US) examination data, and electronic health records (EHRs) of pregnant women. Methods The model integrates three modalities of data from 105 pregnant women (76 vaginal deliveries and 29 cesarean deliveries) at the Department of Obstetrics and Gynecology of The First Affiliated Hospital of Jinan University, Guangzhou, China. It employs a hybrid architecture of a convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) to compress the data into a single feature vector for each patient. Results The designed model achieves a cross-validation accuracy of 93.33%, an F1-score of 86.26%, an area under the receiver operating characteristic curve of 97.10%, and a Brier Score of 6.67%. Importantly, while cCTG and EHRs are crucial for labor management, the integration of US imaging data significantly enhances prediction accuracy. Conclusion The findings of this study suggest that the developed multimodal model is a promising tool for predicting delivery mode and provides a comprehensive approach to intrapartum maternal and fetal health monitoring. The integration of multi-source data, including real-time information, holds potential for further improving the algorithm's predictive accuracy as the volume of analyzed data increases. This could be highly beneficial for dynamically fusing data from different sources throughout the maternal and fetal health lifecycle, from pregnancy to delivery. |
| format | Article |
| id | doaj-art-7b78cffaada741cdbfa8d197f9735e61 |
| institution | OA Journals |
| issn | 2055-2076 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-7b78cffaada741cdbfa8d197f9735e612025-08-20T02:30:39ZengSAGE PublishingDigital Health2055-20762024-12-011010.1177/20552076241304934A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring systemJieyun Bai0Xue Kang1Weishan Wang2Ziduo Yang3Weiguang Ou4Yuxin Huang5Yaosheng Lu6 , Auckland, New Zealand Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, , Guangzhou, China Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, , Guangzhou, China Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, , Guangzhou, China , Jinan University, Guangzhou, China Department of Obstetrics and Gynecology, Zhujiang Hospital, , Guangzhou, China Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, , Guangzhou, ChinaObjective This study aims to address the limitations of current clinical methods in predicting delivery mode by constructing a multimodal neural network-based model. The model utilizes data from a digital twin-empowered labor monitoring system, including computerized cardiotocography (cCTG), ultrasound (US) examination data, and electronic health records (EHRs) of pregnant women. Methods The model integrates three modalities of data from 105 pregnant women (76 vaginal deliveries and 29 cesarean deliveries) at the Department of Obstetrics and Gynecology of The First Affiliated Hospital of Jinan University, Guangzhou, China. It employs a hybrid architecture of a convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) to compress the data into a single feature vector for each patient. Results The designed model achieves a cross-validation accuracy of 93.33%, an F1-score of 86.26%, an area under the receiver operating characteristic curve of 97.10%, and a Brier Score of 6.67%. Importantly, while cCTG and EHRs are crucial for labor management, the integration of US imaging data significantly enhances prediction accuracy. Conclusion The findings of this study suggest that the developed multimodal model is a promising tool for predicting delivery mode and provides a comprehensive approach to intrapartum maternal and fetal health monitoring. The integration of multi-source data, including real-time information, holds potential for further improving the algorithm's predictive accuracy as the volume of analyzed data increases. This could be highly beneficial for dynamically fusing data from different sources throughout the maternal and fetal health lifecycle, from pregnancy to delivery.https://doi.org/10.1177/20552076241304934 |
| spellingShingle | Jieyun Bai Xue Kang Weishan Wang Ziduo Yang Weiguang Ou Yuxin Huang Yaosheng Lu A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system Digital Health |
| title | A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system |
| title_full | A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system |
| title_fullStr | A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system |
| title_full_unstemmed | A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system |
| title_short | A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system |
| title_sort | multimodal model in the prediction of the delivery mode using data from a digital twin empowered labor monitoring system |
| url | https://doi.org/10.1177/20552076241304934 |
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