• Ramandeep Singh, Rajdeep Singh, Chander Sen, Umesh Gautam, Sitikantha Roy, and Ashish Suri.
• Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India.
• World Neurosurg. 2023 Jan 1; 169: e197e205e197-e205.

BackgroundSimulation-based neurosurgical training allows the development of surgical skills outside the operating room. However, the use of nonstandardized materials and poor haptic feedback remain the primary limitations of the surgical simulators. Therefore, this work proposes a comprehensive scheme for scalp and dura surrogate synthesis and their standardization for neurosurgical training.MethodsEight different variants of silicone-based scalp (S1-S8) and dura (D1-D8) surrogates were synthesized. The samples were evaluated by 26 neurosurgeons. They provided their feedback in a Likert scale questionnaire. Kruskal-Wallis test with Dunn multiple comparisons was used for statistical analysis of surgeons' scores. The samples were mechanically characterized using Shore A hardness and dynamic nanoindentation testing.ResultsThe highest mean Likert score values were obtained for S3 scalp and D8 dura variants. The comparison of S3 and D8 with the rest of the variants in the respective groups was statistically significant in 21 of 28 instances. The average Shore A hardness and storage modulus of the S3 variant were 21.9 DU and 505.3 kPa, respectively. The corresponding values for the D8 variant were 32.5 DU and 632 kPa, respectively.ConclusionsThis study proposes a method for the synthesis, evaluation, and standardization of scalp and dura surrogates. The study achieved standardized silicone compositions along with a recommendable range of Shore hardness and viscoelastic moduli values for the scalp and dura surrogates. This work can be extended for the standardization of surrogates for other tissues involved in neurosurgical simulators.Copyright © 2022. Published by Elsevier Inc.

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