Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery

Abstract

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.

INTRODUCTION

Precision medicine is becoming central to pediatric neurosurgery, focusing on identifying diseases by their molecular causes, customizing surgical approaches based on functional brain anatomy and developmental factors, and coordinating additional treatments according to individual risk profiles. Nevertheless, there remains an ongoing obstacle that limits the practical application of precision medicine. The clinician must reconcile multi-modal, multi-scale, and longitudinal data into a coherent patient-specific plan, while communicating uncertainty and adapting as new information emerges. The challenge is no longer an absence of patient-specific data but the integration of these data into decisions.

Digital twin (DT) offers a pragmatic way to bridge this gap. A DT is a virtual representation of an individual that is linked to measured data and used in an operational loop. Within this loop, measured data update the model, which then supports predictions and simulations of candidate interventions. Subsequent clinical outcomes feed back into the DT and recalibrate it over time [45,84,108]. This loop matters more in children than in adults because growth and neurodevelopment create natural drift, and because the opportunity to change lifetime trajectories is greater. DTs also align with a broader movement toward integrating mechanistic simulation and machine learning (ML) into clinical decision support [56,95]. When implemented conservatively, DTs can help clinicians pose counterfactual questions in a structured, transparent way.

This review outlines the defining components of DTs and explains why pediatric neurosurgery amplifies both their value and their risks [3,18,84]. Application domains are organized around concrete clinical questions, data requirements, and model types. A translational roadmap is then proposed to support safe development, validation, and clinical deployment of DTs in children.

DTs : DEFINITION AND KEY COMPONENTS

A practical clinical definition of a medical DT is “a viewable digital replica” of a patient, organ or biological system containing multidimensional, patient-specific information that aids in the decision-making process [24]. Many neurosurgical tools already incorporate models, including finite element simulations for craniofacial surgery, computational fluid dynamics (CFD) for cerebrospinal fluid (CSF) systems, and ML risk scores. The distinguishing feature of a DT is not mathematical sophistication alone but operational coupling to data and repeated decision- making over time [108]. A DT is built to be updated, audited, and evaluated across multiple time points. This distinction is particularly relevant for children because growth and development change anatomy and physiology even without disease progression.

Recent medical DT frameworks emphasize five components. To begin, consider the patient : clinical objectives, limitations, and the biological system to be represented. Second, data connection : channels used to capture and standardize signals such as imaging, electrophysiology, pathology, omics, device telemetry, and outcomes. Third, a patient-in-silico model : a representation that integrates data into latent states and dynamics relevant to decisions, often combining mechanistic constraints and data-driven inference19,56). Fourth, a clinical interface : a layer that translates model states and simulations into actionable outputs, such as scenario comparisons and uncertainty. Fifth, synchronization : processes that keep the DT aligned with the patient over time, including data assimilation, recalibration, drift detection, and model version control [84]. Fig. 1 describes the five components. Table 1 maps these components to pediatric neurosurgical examples and requirements.

Fig. 1.

Five-component medical digital twin (DT) loop. Schematic overview of a clinical DT as an end-to-end loop linking the patient, data connection, patient-in-silico model, and clinical interface, with continuous synchronization to keep the DT aligned with the evolving patient state. Multimodal data are collected and standardized, then integrated into mechanistic or artificial intelligence (AI)-based models that reveal hidden patterns and behaviors. The clinical interface presents actionable outputs such as scenario comparisons and uncertainty estimates, while synchronization encompasses assimilation, recalibration, drift detection, and version control to maintain an up-to-date and auditable representation over time. Figure created with FigureLabs (http://www.figurelabs.ai).

Table 1.

Core components of a medical digital twin and interpretation in pediatric neurosurgery

Component	Epilepsy	Neuro-oncology	Cerebrovascular disease	Hydrocephalus	Craniosynostosis
Patient	Seizure freedom	Survival	Ischemic/hemorrhagic risk	Symptom control	Shape
	Function	Cognitive preservation	Procedural morbidity	Device burden	ICP
			Patient-specific vessel geometry		Growth/development
			Fluid dynamics
Data connection	Diffusion connectome	MRI/CT	Vascular imaging (MRA/CTA/DSA)	MRI/CT	Skull radiography
	EEG/SEEG	Pathology	Perfusion imaging	Shunt valve settings	CT
		Methylation	Phase-contrast or 4D-flow MRI	Catheter design parameters	Black bone MRI
		Genomics	Doppler-derived velocities
			Draining-vein velocity
			Bypass configuration
			Anastomosis geometry
			Endovascular device
Patient-in-silico model	Virtual brain seizure propagation/network	Tumor growth	Patient-specific CFD/FSI	CSF flow/shunt transport	Cranial biomechanics
		Hypoxia	Lumped-parameter AVM network		3D modeling
		Metabolism	Fast surrogate hemodynamics	Device-aware simulation
		Treatment-response
Clinical interface	Comparison of candidate resections with uncertainty	Therapy prioritization	Bypass planning with predicted perfusion change	Catheter trajectory	Predicted growth trajectories after cranial surgery
		Trial matching	AVM embolization strategy simulation	Device scenario testing
			Stent/flow-diverter placement scenarios
			Hemodynamic risk stratification with uncertainty
Synchronization	Postoperative MRI/EEG updating	Therapy response imaging	Serial angiography/perfusion	Valve adjustment	Serial head measurements/imaging
		Molecular reassessment	Hemodynamic reassessment after bypass/embolization/stenting	Symptom follow-up	Postoperative remodeling
			Interval ischemic/hemorrhagic events as update triggers	Revision events

ICP : intracranial pressure, EEG : electroencephalography, SEEG : stereoelectroencephalography, MRI : magnetic resonance imaging, CT : computed tomography, MRA : magnetic resonance angiography, CTA : computed tomography angiography; DSA : digital subtraction angiography, 4D : 4-dimensional, CFD : computational fluid dynamics, FSI : fluid-structure interaction, AVM : arteriovenous malformation, CSF : cerebrospinal fluid, 3D : 3-dimensional

To reduce ambiguity in clinical use, where DT is sometimes used interchangeably with virtual planning or risk scoring, minimum operational criteria and a pragmatic readiness classification for a clinical DT are proposed [18,48,54,77]. Table 2 presents the minimum operational criteria, and all these criteria must be met. Fig. 2 delineates readiness levels.

Table 2.

Minimum operational criteria for a clinical digital twin

Criteria	Details
Identifiable patient representation tied to the clinical question	Anatomy
	Physiology
	Biology
Defined data connection	Cadence update
	Data provenance
Patient-in-silico model	Generating counterfactual outputs
Explicit uncertainty characterization and detection	Distribution shift
	Out-of-distribution inputs
Clinician-facing interface	Supporting human oversight
	Recording inputs/outputs
Auditability	Version control
	Logging
	Traceability sufficient for retrospective review
Performance monitoring plan across time and sites	Recalibration
	Rollback
	Decommissioning

Fig. 2.

Clinical digital twin (DT) readiness levels. Proposed maturity scale for clinical DTs. Level 0 represents a static model that captures a single snapshot and is not updated. Level 1 describes a single-use planning model that is patient-specific but not prospectively updated. Level 2 denotes a prospective shadow or silent DT that runs in the intended workflow without influencing care. Level 3 refers to an operational DT that provides supervised decision support with documented human-in-the-loop use. Level 4 defines a learning DT that undergoes iterative updates under a predetermined change control plan and post-deployment monitoring. Figure created with FigureLabs (http://www.figurelabs.ai).

PEDIATRIC-SPECIFIC REQUIREMENTS AND RISKS

Pediatric DT faces requirements that are not simply scaled-down versions of adult systems [23,26,30]. Pediatric DT translation introduces constraints that extend beyond growth and development. Pediatric neurosurgical conditions are often rare and heterogeneous, amplifying risks of overfitting and limited generalizability [2,27]; therefore, multicenter harmonization, age-stratified evaluation, and explicit reporting of cohort composition are essential. Longitudinal data acquisition may require sedation, impose follow-up burdens on families, and raise unique consent/assent challenges, particularly when DT outputs could influence high-stakes decisions [88]. Consequently, pediatric DT systems must incorporate robust uncertainty quantification and communication strategies tailored to parent-clinician-patient triads. Finally, fairness and safety should be assessed across developmental stages, ensuring that performance does not degrade systematically in underrepresented age or pathology subgroups [41,69].

MODELING FOUNDATIONS

The patient-in-silico component can be implemented with different modeling paradigms. In pediatric neurosurgery, mechanistic models are attractive where physics and anatomy strongly constrain behavior, while data-driven components can infer unknown parameters and bridge biological complexity. Hybrid DT engines that combine mechanistic scaffolds with artificial intelligence (AI) inference are increasingly viewed as practical because they enable counterfactual simulation with data-driven personalization [19,56].

Mechanistic models

Mechanistic models encapsulate principles of physics and physiology. In the fields of neurosurgery, these include classical compartment models of intracranial compliance [96,97], finite element models for skull deformation [6,65,106], CSF flow dynamics [14,75,90], and vascular lesion hemodynamics [11,31]. Such models are interpretable and facilitate counterfactual analysis. Nonetheless, they require assumptions and parameter estimations that may be difficult to derive from limited clinical data, and they often entail high computational costs.

Hybrid approaches with AI

AI serves as an integrative driver that strengthens the entire DT loop, particularly in pediatric neurosurgery where implementations often lean heavily on mechanistic simulations. At the front-end data-connection layer, AI facilitates the harmonization and fusion of heterogeneous multimodal inputs common in pediatric care including structural imaging, physiological signals, molecular profiling, and longitudinal clinical narratives. Automated lesion segmentation, volumetric analysis, and extraction of signal-derived biomarkers transform these data into inputs suitable for modeling. Approaches such as radiogenomics, which connect imaging features to underlying biological states, enable the derivation of clinically meaningful phenotypes and patient stratification [39,111]. Within the patient-in-silico component, AI complements mechanistic modeling by capturing complex, time-varying processes that are difficult to parameterize explicitly or by providing fast surrogates for computationally expensive modules. These hybrid models can be implemented through Bayesian parameter estimation, physics-informed learning, or modular ML components that map multimodal inputs to mechanistic states [46,47,52]. Finally, AI enhances the clinician-facing interface by presenting scenario comparisons and uncertainty estimates. Emerging foundation models, including large language models (LLMs), further facilitate more transparent and actionable decision-making by linking DT outputs to relevant guidelines and supporting evidence [50,84]. When LLMs are incorporated into the interface, their role should be limited to evidence-linked functions such as retrieval-augmented summaries with source citations. They should not generate autonomous treatment recommendations. They should be deployed with safety guardrails including input sanitization, hallucination checks, and explicit communication of uncertainty and limitations to reduce the risk of persuasive but incorrect outputs [1,57,72,103].

UNCERTAINTY QUANTIFICATION AND INTERPRETABILITY

Uncertainty is not a technical nuisance but a clinical output. DT predictions should include uncertainty estimates, sensitivity to assumptions, and indicators of out-of-distribution input [85]. This is essential in pediatrics, where families may interpret algorithmic outputs as deterministic. Approaches such as Bayesian inference, ensemble methods, calibration assessment, and scenario analysis can support uncertainty communication [53,85]. Interpretability is paramount as well : clinicians must discern the specific inputs underpinning a recommendation and observe how model outputs shift in response to altered assumptions [51]. Interpretability and explicit uncertainty quantification make a DT more likely to support shared decision-making than a black-box score.

DISEASE-SPECIFIC APPLICATIONS

Numerous examples specific to neurosurgery are still at a pre-DT stage of development and should be viewed as building blocks that enable DT rather than as fully integrated closed-loop systems. As a result, applying a full DT-loop template is not always possible because longitudinal updating, auditability, and workflow-integrated validation are often absent. Therefore, this part provides a historical overview of the mechanistic and data-driven foundational models that have brought each disease area closer to the DT concept with a brief discussion of the practical barriers and remaining gaps for clinical implementation.

Epilepsy surgery : from personalized networks to virtual intervention testing

Epilepsy surgery is among the most advanced neurosurgical domains for individualized modeling. Personalized virtual brain models have been proposed to guide epilepsy care by incorporating individual connectomes and simulating seizure dynamics [44]. The Virtual Epileptic Patient (VEP) paradigm exemplifies this approach by integrating patient-specific anatomy, structural connectivity, and intracranial electroencephalography data to construct whole-brain network models capable of reproducing seizure-like dynamics and simulating propagation patterns [100]. Latent dynamical states inferred from these models have been linked to seizure onset features and surgical outcomes, supporting their clinical relevance [64]. Building on this foundation, subsequent work has extended the framework toward a non-invasive diagnostic pathway, using stimulation-induced seizures to infer the epileptogenic zone network via insilico brain models, offering a methodological bridge from invasive mapping to model-guided [101]. Importantly, the VEP paradigm has progressed from retrospective evaluation to a prospective multicenter randomized trial : the EPINOV (Improving EPilepsy surgery management and progNOsis using Virtual brain technology) trial (NCT03643016) targets approximately 356 patients with drug-resistant focal epilepsy and integrates VEP outputs into multidisciplinary surgical decision-making [99]. The EPINOV consortium includes a pediatric neurosurgical center, although dedicated pediatric subgroup analyses have not yet been published. In addition to mechanistic modeling, connectome-focused ML has offered valuable predictive tools and supplementary phenotyping markers, which align seamlessly with the DT framework. A work in temporal lobectomy proposed that an individual’s structural connectome can function as a personalized biomarker of postoperative seizure outcome, suggesting that network topology contains prognostic information beyond lesion location alone [5]. Taylor et al. [91] quantified how epilepsy surgery alters structural connectivity and how those changes relate to outcome. Outcome prediction should not be static : surgery itself perturbs the connectome [91]. A larger ML study later showed that surgical outcomes in temporal lobe epilepsy can be inferred from features related to structural connectome hubs, reinforcing the clinical relevance of network centrality and distributed connectivity as decision-support variables [35]. Most demonstrations remain research-grade and stop short of an operational DT loop with prospective workflow integration, standardized data provenance, and repeated recalibration in routine care.

Neuro-oncology : connecting molecular precision to dynamic tumor behavior

Molecular classification provides biologically coherent entities [10,60]. Extent of resection remains a critical neurosurgical variable in many tumors, and its prognostic implications can differ across molecular subgroups [93]. Precision medicine trials like Pediatric MATCH (Molecular Analysis for Therapy Choice) translate genomic data into clinical action by pairing actionable alterations with corresponding targeted agents [74]. Targeted therapies for B-Raf proto-oncogene, serine/threonine kinase (BRAF)-altered pediatric low grade glioma, such as BRAF/mitogen-activated protein kinase (MEK) inhibition and next-generation kinase approaches, have begun to reshape sequences of therapy and surgery [7,49]. Thiong’o and Rutka [92] proposed DT technology as a frontier strategy for identifying predictors of neurological complications in pediatric oncology. The framework of DT enhances diagnostic and prognostic rigor by integrating key clinical themes, including precision medicine, modeling cancer care and research, predictive analytics, and the consolidation of expert opinions. Especially where clinical trial cohorts are limited, the iterative integration of real-time data into virtual models provides a pragmatic bridge for knowledge gaps and individualized interventions. Earlier mathematical oncology studies predicted individual radiotherapy efficacy in glioblastoma and modeled hypoxia-modulated radiation resistance using 18F-fluoromisonidazole PET [82,83]. Chaudhuri et al. [13] suggested a patient-specific DT to personalize radiotherapy for high-grade glioma by calibrating a mechanistic tumor-response model to serial magnetic resonance imaging (MRI) data using Bayesian inference. The calibrated DT predicted time to progression with uncertainty quantification and enables risk-based, multi-objective optimization of fractionation schedules that balance tumor control against toxicity through total dose [13]. A recent study presented a clinically integrated, DT-like framework that is mechanistically grounded and strengthened by AI [67]. It estimated in vivo metabolic fluxes in brain cancer by integrating isotope tracing, non-steady-state metabolic flux analysis, convolutional neural networks, and single-cell RNA-seq-informed flux inference. The authors demonstrated that serine sourcing varies among patients and utilized this mechanistic stratification to justify targeted interventions, such as dietary restriction and the inhibition of de novo purine synthesis with mycophenolate mofetil. This approach suggests the existence of patient-specific therapeutic windows and aligns with established links between purine metabolism, DNA repair, and glioblastoma therapy resistance [112]. Pediatric-specific evidence and prospective workflow evaluations are comparatively limited, and DT outputs must be aligned with toxicity-sensitive endpoints relevant to long survivorship horizons.

Cerebrovascular disease : hemodynamic DTs for flow-guided decisions

Cerebrovascular disorders are well suited to DT-type approaches becausekey clinical risks and treatment effects are mediated by flow, pressure, and wall shear stresses that can be simulated from patient-specific vascular anatomy. A practical neurovascular DT typically links vascular imaging to a patient-in-silico model for scenario simulation, then updates as anatomy and physiology evolve. In pediatrics, this loop is relevant because collateral development and treatment consequences unfold over years, creating natural opportunities for synchronization with repeat imaging and clinical events. However, key limitations include reliable boundary conditions, computational burden, and the need for longitudinal pediatric validation that connects simulated states to clinically meaningful outcomes and decision impact.

Moyamoya disease (MMD)

MMD illustrates how patient-specific CFD can function as an updateable latent state. Horn et al. [40] reconstructed patient-specific Circle of Willis geometries in pediatric MMD and performed Navier–Stokes simulations, deriving wall shear rate (WSR)-based metrics intended to quantify contralateral stroke risk. Their results indicate higher contralateral WSR burden in children who later developed early contralateral stroke. The authors also suggested that repeating patient-specific hemodynamic analysis across follow-up time points could improve risk estimation by tracking how WSR metrics evolve with progression or stabilization [40]. Subsequent works have extended patient-specific modeling toward treatment planning in MMD, especially for superficial temporal artery-middle cerebral artery bypass. Wang et al. [102] quantified hemodynamic changes after bypass and proposed simulation as a noninvasive tool to assess treatment efficacy. Peng et al. [78] evaluated the role of anastomosis geometry including graft angle on hemodynamics after direct bypass, highlighting how modifiable surgical parameters can be explored through “what-if ” comparisons before or alongside operative decision-making. Prognostic modeling connecting morphology and hemodynamics to outcomes further supports combining structure with flow-derived states for individualized risk stratification [63].

Arteriovenous malformations (AVMs)

In AVMs, recent work spans complementary levels of detail in modeling. Lumped-parameter models represented patient-specific network-level flow redistribution using multimodal inputs [110]. A CFD-based study strengthened boundary conditions of simulation by incorporating patient-derived measurements such as draining-vein velocities extracted from imaging [62]. Procedural planning is also moving toward DT-like “what-if” evaluation. Workflow-oriented frameworks were proposed for simulating AVM embolization and testing alternative procedural strategies in silico [109]. Stahl et al. [89] suggested multimodal imaging pipelines to standardize how patient-specific AVM data are assembled and processed to support individualized analysis.

Aneurysms

Patient-specific CFD was applied to evaluate bypass strategies for complex or giant aneurysms as a form of preoperative scenario simulation [105]. Rajhi et al. [81] showed that stent-induced geometric deformation can alter hemodynamics, supporting individualized modeling to guide stent design and placement for improved clinical outcomes. Beyond single-case simulations, review-level research has continued to link CFD-derived metrics to rupture risk, reinforcing the clinical motivation for individualized hemodynamic phenotyping [9]. Emerging surrogate approaches, including physics-constrained graph neural network models, are being developed to approximate expensive hemodynamic simulations at near real-time speeds, enabling rapid scenario exploration [55]. Goetz et al. [36] introduced a reproducible open dataset of 101 semi-idealized sidewall aneurysms and showed that deformable-wall flow-structure interaction (FSI) can markedly alter hemodynamic metrics compared with rigidwall CFD, highlighting the decisive impact of FSI modeling on predicting treatment outcome.

Hydrocephalus : tailoring device behavior and anticipating failure

Hydrocephalus is an optimal candidate for pediatric DT applications, given the frequency of shunt decisions and high failure rates. Recent data-driven work already approximates “DTlike” risk stratification : ML models predict shunt failure, enabling individualized surveillance [38]. Large-scale predictive models in shunted children facilitate the operationalization of outcome-relevant predictions [87]. Digital shunt registries have been proposed to standardize data collection, crucial for iterative model refinement [4]. Automated imaging phenotyping enhances routine scan utility, with convolutional neural networks employed for ventricular segmentation and failure detection [42]. On the mechanistic front, patient-specific simulation frameworks are evolving to enable scenario comparisons. CFD techniques have been utilized in ventricular catheter design to enhance flow characteristics [28]. Initial clinical trials of novel catheter designs suggest a correlation between simulation results and actual clinical outcomes [29]. FSI simulations in non-communicating hydrocephalus model treatment effects on CFD, revealing links between pressure metrics and clinical symptoms [33]. Research has focused on assessing the impact of varying boundary conditions on CSF simulations, yielding insights for creating robust, individualized models [34]. Recent validation of ventricular-system models against advanced flow imaging corroborates the potential for individualized fluid dynamics informed by data [75]. A clinically usable hydrocephalus DT would combine these strands into a synchronized loop : initialize from imaging plus device parameters, simulate flow and transport under a small set of plausible choices, and then update using follow-up imaging and clinical events. Quantitative, noninvasive measurements of CSF flow in shunted hydrocephalus using phase-contrast MRI provide a concrete update signal that can anchor recalibration after valve changes or revisions [37]. Device-facing evaluation methods can also support translation; patient-specific hardware-in-the-loop testing of CSF shunt systems illustrates how individualized device behavior can be interrogated under controlled, reproducible conditions [32]. Notably, several elements of this loop are already operating prospectively in children. Telemetric intracranial pressure (ICP) monitoring with implantable sensors has been deployed in pediatric cohorts. In these cohorts, serial home recordings guide non-surgical valve adjustments, providing a clinically functioning data-connection-and-recalibration loop that maps onto the synchronization element of a DT [76]. A recent first-in-human trial evaluated a 0.28-g implantable ICP microimplant for up to 18 months of remote home monitoring in 10 adults and 10 children with hydrocephalus, and pediatric shunt failures were detected from home recordings. This represents the closest current implementation of the data-connection layer of a pediatric hydrocephalus DT [66]. Neither system is yet coupled to a patient-in-silico model, however, so each remains a partial rather than complete pediatric DT. This framework delineates a hydrocephalus DT model comprising mechanistic simulations, ML for risk evaluation, and integrated registries and sensors. Clinical translation depends on an end-to-end, unified pipeline that links data streams, device settings, and model outputs, along with prospective validation to confirm robustness and real-world clinical benefit.

Craniosynostosis : growth prediction and surgical planning

Craniosynostosis is already one of the most “digital-ready” pediatric neurosurgical domains because individualized cranial geometry must be translated into definite operative choices such as osteotomy design, segment repositioning, fixation strategy, and timing [73,80]. Contemporary computerized surgical planning commences with patient-specific biomodeling and 3-dimensional (3D) imaging and printing. It advances through virtual osteotomies and cranial vault reconstruction [8,15,22,59,70,73]. Recent examples include computer-aided design and virtual surgical planning for unilateral lambdoid synostosis, illustrating the transformation of individualized anatomical data into preplanned correction strategies rather than intraoperative adjustments [58]. The patient-in-silico component becomes more explicit when planning moves to forward prediction of growth, remodeling, and treatment response. Finite element-based approaches have been proposed to predict head reshaping after endoscopic strip craniectomy and helmet therapy [21]. Parallel advances seek to diminish dependence on computed tomography by applying “black bone” MRI for 3D evaluation of craniosynostosis and implementing computer-assisted workflows, which are significantly important in pediatrics given the ongoing challenge of radiation stewardship in long-term monitoring [25]. Even if a fully realized DT remains a longer-term goal, the underlying building blocks are spreading widely. AI can complement this ecosystem by accelerating phenotype inference, automating severity quantification, and supporting prediction tasks that are difficult to specify mechanistically. AI and ML have been applied to craniosynostosis diagnosis, operative planning, severity assessment, and outcome prediction. However, small and heterogenous datasets can lead to algorithmic bias [61,79]. To overcome the limitations of clinician-facing interfaces, new tools are emerging to enhance the visualization and intraoperative transfer of virtual plans. These include comparative studies of surgical guides versus augmented reality, alongside broader applications of extended reality [12,68]. The broader adoption of DT-based synthesis in plastic and reconstructive surgery highlights the transformative potential of patient-specific simulations. However, it also brings to light practical hurdles, such as cost, system integration, and governance [86].

Pediatric-specific constraints across disease domains

Across these applications, pediatric-specific constraints affect what should be synchronized, validated, and communicated in each disease domain. In epilepsy surgery, age-dependent brain connectivity and neurodevelopmental outcomes require pediatric-specific calibration rather than direct extrapolation from adult cohorts. In neuro-oncology, rare molecular subgroups and long survivorship horizons require DT outputs to be evaluated against toxicity-sensitive, cognitive, and functional endpoints. In cerebrovascular disease, dynamic collateral development and long-term vascular remodeling make serial validation essential, but they also increase imaging burden and boundary-condition uncertainty. In hydrocephalus, device adjustments, symptom trajectories, and ventricular morphology must be interpreted within age-dependent physiology and family-mediated monitoring contexts. In craniosynostosis, normal skull growth, radiation stewardship, and timing-sensitive developmental outcomes make longitudinal, low-burden synchronization and age-stratified validation central to clinical translation.

TRANSLATIONAL ROADMAP FOR BUILDING DTs

Stage 1 : problem selection and retrospective validation

Select problems where simulation informs a concrete decision and outcomes are measurable. Define the intended use, decision point, comparator workflow, and target population a priori. Build a retrospective DT prototype with clear reporting, bias assessment, and sensitivity analysis [16,107]. Whenever feasible, include external validation across sites or time periods to expose center effects that are common in pediatric care.

Stage 2 : prospective “silent mode” evaluation

Deploy the DT in the intended clinical environment to run prospectively without influencing decisions. This phase evaluates end-to-end reliability: data availability, pipeline robustness, runtime errors, latency, and performance under real-world distribution shift. Measure calibration, stability under shift, and usability. In parallel, perform human-factors evaluation of how outputs would be interpreted, and refine interface design to support shared decision-making and safe escalation [17,54,94]. This stage is particularly important in pediatrics, where small cohorts and center effects can distort performance.

Stage 3 : interventional evaluation and learning health systems

When DT outputs influence clinical decisions, evaluation should follow AI trial standards and use designs appropriate to workflow interventions [20,43,98]. Because DTs may evolve with new data, translation should include a lifecycle plan : predefined update rules, versioning, audit logs, post-deployment monitoring for integrity/performance/impact, and clear criteria for rollback or decommissioning. Ultimately, DTs should be embedded into learning systems where outcomes feed back into model recalibration [43,48,108].

At present, virtually all pediatric neurosurgical applications discussed above remain at stage 1. A small number of examples are beginning to emerge into stage 2, as illustrated by prospective telemetric ICP monitoring in pediatric hydrocephalus cohorts [66,76]. At stage 3, the EPINOV trial represents the most advanced neurosurgical evaluation currently underway [99], although it is conducted in a mixed-age cohort that includes pediatric centers rather than a pediatric-specific population. To date, no pediatric-specific DT has completed a full stage 3 interventional evaluation.

CHALLENGES AND FUTURE DIRECTIONS

Open challenges for DTs in pediatric neurosurgery can be grouped into data, validity, usability, and equity. Because many pediatric neurosurgical conditions are rare, available cohorts are often small, which increases the risk of overfitting and limits generalizability. To address this, multi-center governance and interoperable infrastructure are essential, supported by ‘Findable, Accessible, Interoperable, and Reusable’ data practices and standards-based data access to improve reuse and external validation [104]. DTs are often expected to enable scenario-based simulation, but credible “what-if ” reasoning requires explicit assumptions and systematic sensitivity analyses. Hybrid approaches that combine mechanistic structure with causal reasoning may improve the plausibility of simulated alternatives compared with purely statistical models. Clinical adoption also depends on human factors and accountability. Interfaces should support shared decision-making by communicating uncertainty clearly. Clinicians retain responsibility. DTs should provide traceable evidence, explicit versioning, and auditable records. Finally, equity remains a core concern, since algorithmic bias can harm vulnerable groups [71]. Pediatric DTs should therefore include equity audits and be validated across diverse ages, etiologies, and care settings.

CONCLUSION

Precision medicine in pediatric neurosurgery is already real at the level of measurement. However, it still needs a practical implementation layer that translates heterogeneous individualized data into actionable, testable decisions. DTs can couple patient-specific data streams to updateable models that simulate alternative interventions within the same child and quantify uncertainty. Emerging work in virtual brain modeling, molecular tumor classification, and patient-specific simulation of devices and growth suggests a feasible path from models to operational DTs. Most pediatric examples in the current literature remain pre-DT components rather than fully realized closed-loop DTs, with some selected domains now approaching deployed implementation. With rigorous validation, conservative deployment, and governance tailored to children, DTs can make precision pediatric neurosurgery more transparent and testable. They can help clinicians and families to compare plausible counterfactual options with a clearer view of uncertainty.