Pediatric neurosurgery increasingly utilizes precision medicine, but practitioners encounter challenges in translating complex data into individualized care. Digital twin (DT) bridges this gap by linking real-world data to a dynamic patient in-silico model, facilitating prediction and adaptive management as new data emerge. This narrative review explores the essential features of DTs, highlighting their relevance and associated risks in pediatric neurosurgery. The DT framework is structured around five components: the patient, a data connection, a patient-in-silico model, a clinician interface, and temporal synchronization. Foundational modeling approaches are summarized, spanning mechanistic simulations, artificial intelligence, and hybrid models that combine mechanistic structure with data-driven inference. Clinical translation is framed around uncertainty and calibration, along with interpretability and detection of distribution shifts. Potential applications are organized by concrete clinical questions in epilepsy surgery, pediatric neuro-oncology, cerebrovascular disease, hydrocephalus, and craniosynostosis. A translational pathway is outlined that progresses from decision-oriented prototypes and retrospective validation to prospective evaluation and interventional studies within learning health systems, supported by robust governance and auditable workflows. With meticulous validation and cautious deployment tailored to pediatric populations, DTs may enhance transparency, testability, and shared decision-making in precision pediatric neurosurgery.
Eun Jung Koh (Fri,) studied this question.