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Digital Twins for Monitoring Neuromotor Development in Preterm Infants: Conceptual Framework and Proof-of-concept
Sara Montagna1, Rita Stagni2, Giada Pierucci3
1Department of Pure and Applied Sciences, University of Urbino, Urbino, Italy. sara.montagna@uniurb.it.
Insights
Digital Twins (DTs) offer a novel approach to monitor infant motor development, crucial for predicting neurodevelopmental disorders (NDDs). This technology integrates multimodal data for holistic, longitudinal evaluations, improving early identification of developmental deviations.
Area of Science:
- Pediatric Neurodevelopment
- Computational Medicine
- Infant Motor Assessment
Background:
- Preterm birth significantly increases risks for motor, cognitive, and behavioral impairments in children.
- Early motor development monitoring is vital for predicting neurodevelopmental disorders (NDDs).
- Current assessment methods lack quantitative, standardized measures and struggle with integrating complex medical histories.
Purpose of the Study:
- To introduce a novel framework using Digital Twins (DTs) and advanced metrics for holistic, longitudinal infant motor development assessment.
- To overcome limitations in current infant motor assessment by integrating multimodal data and sophisticated analysis.
- To support early identification and prediction of neurodevelopmental disorders in infants.
Main Methods:
- Development of a Digital Twin (DT) ecosystem integrating data processing algorithms and AI for analysis.
- Implementation of a proof-of-concept application using published data from full-term, preterm, and very preterm infants.
- Standardization of motor, clinical, and cognitive data within the DT model for advanced analyses.
Main Results:
- Demonstrated the feasibility of the DT framework in integrating diverse infant data (motor, clinical, cognitive).
- Showcased initial advanced analyses integrating standardized measures within the DT.
- Validated the potential for a scalable and adaptable solution for infant monitoring.
Conclusions:
- The proposed DT approach offers a significant advancement for clinical practice in monitoring preterm infant motor trajectories.
- This scalable framework can be adapted for large, multi-center studies across various infant populations requiring motor function monitoring.
- DTs provide a powerful tool for holistic, longitudinal evaluations, enhancing the prediction and management of NDDs.
Abstract:
Preterm birth leads to an increased risk of long-term consequences, with over 50% of children born <30 weeks facing motor, cognitive, or behavioural impairments. Early monitoring of motor developmental trajectories, strongly associated with neurodevelopmental outcome, is crucial for a timely identification of deviations from the reference path and the prediction of possible neurodevelopmental disorders (NDDs). However, the current understanding of the causal pathways through which motor difficulties emerge and evolve is limited by the lack of quantitative, standardised, and interpretative measures for infant motor development, and the need for a complex multidisciplinary examination of medical history. To overcome these limitations, we propose an approach based on Digital Twins (DTs) and innovative technology-based interpretative metrics for motor assessment to support holistic longitudinal evaluations of infant development. The DT enables the integration of multimodal data, including algorithms for data processing and artificial intelligence methods for data analysis, into a unique framework. Details on the DT ecosystem, internal model, and engine are provided. As a first step, a proof-of-concept application was implemented to show the feasibility of the framework, not yet exploring its full longitudinal potential. This initial study was based on already published data (17 full-term children, 21 preterm children born between 29 and 36 gestational weeks, and 8 very preterm children born ≤28 gestational weeks) and illustrates the integration of motor measures with clinical and cognitive information, their standardisation into the DT model, and a first set of advanced analyses. Given the relevance of the problem and the lack of standardised, structured follow-up protocols to monitor motor trajectory in preterm children, the proposed solution has the potential for a significant impact in clinical practice. Moreover, its usable and scalable design allows for easy adaptation to large, multi-center cohort studies targeting various infant clinical populations where motor function monitoring is essential (i.e. from children with rare neurological disorders to all newborns).
