Objectives The objectives of this manuscript were to review the literature concerning 3D printing of brain and cranial vault pathology and use these data to define the gaps in global utilization of 3D printing technology for neurosurgical education. Methods Using specified criteria, literature searching was conducted to identify publications describing engineered neurosurgical simulators. Included in the study were manuscripts highlighting designs validated for neurosurgical skill transfer. Purely anatomical designs, lacking aspects of surgical simulation, were excluded. Eligible manuscripts were analyzed. Data on the types of simulators, representing the various modelled neurosurgical pathologies, were recorded. Authors’ countries of affiliation were also recorded. Results A total of thirty-six articles, representing ten countries in five continents were identified. Geographically, Africa as a continent was not represented in any of the publications. The simulation-modelling encompassed a variety of neurosurgical subspecialties including: vascular, skull base, ventriculoscopy / ventriculostomy, craniosynostosis, skull lesions / skull defects, intrinsic brain tumor and other. Finally, the vascular and skull base categories together accounted for over half (52.8 %) of the 3D printed simulated neurosurgical pathology. Conclusions Despite the growing body of literature supporting 3D printing in neurosurgical education, its full potential has not been maximized. Unexplored areas of 3D printing for neurosurgical simulation include models simulating the resection of intrinsic brain tumors or of epilepsy surgery lesions, as these require complex models to accurately simulate fine dissection techniques. 3D printed surgical phantoms offer an avenue for the advancement of global-surgery education initiatives.

OBJECT The purposes of this study were to evaluate the frequency with which children presented with ventriculoperitoneal (VP) shunt perforations of the gastrointestinal (GI) tract, to determine the type of shunts that caused the perforations, and to compare the stiffness of perforating catheters with the stiffness of catheters from other manufacturers. METHODS Medical records were reviewed of 197 children who were admitted with VP shunt malfunction. Catheter stiffness was evaluated by measuring relative resistance to cross-sectional compression, resistance to column buckling, and elasticity in longitudinal bending. Catheter frictional force was measured per unit length. RESULTS Six children were identified whose VP shunts had perforated the GI tract; 2 shunts subsequently protruded through the anal orifice, 1 protruded through the oral cavity, and 3 presented with subcutaneous abscesses that tracked upward from the intestine to the chest. All perforating shunts were Chhabra shunts. Catheter stiffness and resistance to bending were greatest with a Medtronic shunt catheter, intermediate with a Codman catheter, and least with a Chhabra catheter. Frictional force was greatest with a Chhabra catheter and least with a Medtronic catheter. CONCLUSIONS The frequency of perforations by Chhabra shunts appears to be higher than the frequency associated with other shunts. The increased frequency does not correlate with their stiffness but may reflect their greater frictional forces.

Healthcare technologies have seen a surge in utilization during the COVID 19 pandemic. Remote patient care, virtual follow-up and other forms of futurism will likely see further adaptation both as a preparational strategy for future pandemics and due to the inevitable evolution of artificial intelligence. This manuscript theorizes the healthcare applications of digital twin technology. Digital twin is a triune concept that involves a physical model, a virtual counterpart, and the interplay between the two constructs. This interface between computer science and medicine is a new frontier with broad potential applications. We propose that digital twin technology can exhaustively and methodologically analyze the associations between a physical cancer patient and a corresponding digital counterpart with the goal of isolating predictors of neurological sequalae of disease. This proposition stems from the premise that data science can complement clinical acumen to scientifically inform the diagnostics, treatment planning and prognostication of cancer care. Specifically, digital twin could predict neurological complications through its utilization in precision medicine, modelling cancer care and treatment, predictive analytics and machine learning, and in consolidating various spectra of clinician opinions.