Maternal and fetal health in the digital twin era

Calcaterra, Valeria; Pagani, Valter; Zuccotti, Gianvincenzo

doi:10.3389/fped.2023.1251427

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…They are regularly updated with information from their real-life counterparts and are characterized by their predictive functionality. The validity of a DT can be verified, making it an invaluable tool for decision-making, providing critical insights to inform the delivery of health and wellness care ( 8 ). At the heart of the digital twin concept is the dynamic, two-way communication between the virtual and physical realms.…”

Section: Digital Twin In Fetal-maternal Healthmentioning

confidence: 99%

“…However, significant challenges hinder the efficacy and affordability of existing interventions. As such, this synopsis aggregates the recent breakthroughs (5) and methodologies (6)(7)(8)(9)(10)(11), delves into potential impact on improving maternal and infant safety, and reflects on how these advancements may guide future academic research.…”

Section: Introductionmentioning

confidence: 98%

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Editorial: New technologies improve maternal and newborn safety

Bai,

Lu,

Liu

et al. 2024

Front. Med. Technol.

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Section: Digital Twin In Fetal-maternal Healthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Editorial: New technologies improve maternal and newborn safety

Bai,

Lu,

Liu

et al. 2024

Front. Med. Technol.

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A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system

Bai,

Kang,

Wang

et al. 2024

DIGITAL HEALTH

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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.

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Tailored Therapy Regimes Using Digital Twins

Sathish,

Ranjan,

Mahek

2024

Advances in Medical Technologies and Clinical Practice

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A huge amount of data needs to be integrated and processed in the field of personalised medicine. In this case, the authors propose a solution that relies on the creation of digital twins. These are high resolution models of individual patients who have been computationally treated with thousands of drugs in order to find the drug that is most suitable for them. Digital twins could improve the proactiveness and individualization of healthcare services. It possesses the capability to identify irregularities and evaluate health risks prior to the onset or manifestation of a disease through the use of prediction algorithms and real-time data. Enormous databases of medical records biological and genomic data interconnected around the world by harnessing the power of super computers provides us the knowledge to create digital twins of yourself and using your data to improve the network for others after you who tend to have diseases that happen together based on similar gene expression or due to unprovoked side effects of simultaneous drug administration.

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Maternal and fetal health in the digital twin era

Cited by 3 publications

References 23 publications

Editorial: New technologies improve maternal and newborn safety

Editorial: New technologies improve maternal and newborn safety

A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system

Tailored Therapy Regimes Using Digital Twins

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