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S163: ADVANCING REMOTE SKULL BASE ANATOMY EDUCATION WITH HIGH-FIDELITY 3D COMPUTER GRAPHICS: APPLICATION IN THE ENDOSCOPIC ENDONASAL APPROACH
Tatsuya Uchida¹; Yuanzhi Xu¹; Yuhei Sangatsuda¹; Erik Burgos-Sosa¹; Vera Vigo¹; Masaki Ikegami¹; Taichi Kin²; Nobuhito Saito²; Aaron Cohen-Gadol³; Juan Fernandez-Miranda¹; ¹Department of Neurosurgery, Stanford University; ²Department of Neurosurgery, The University of Tokyo; ³Department of Neurological Surgery, Keck School of Medicine of USC

Background: In skull base surgery, particularly in the endoscopic endonasal approach (EEA), precise manipulation of delicate neurovascular structures within a deep and narrow surgical corridor requires a thorough three-dimensional understanding of the anatomy. Cadaveric dissection remains the gold standard for anatomical training, but its widespread use is limited by high costs, the need for specialized facilities, ethical considerations, and restricted opportunities for repeated practice. With the rapid growth of remote education and diversification of learning styles, there is an increasing demand for flexible and sustainable resources. Virtual dissection (VD) environments using high-fidelity three-dimensional computer graphics (3DCGs) have emerged as promising alternatives, yet few systems have been systematically developed and evaluated for EEA anatomy.

Objective: To develop a high-fidelity 3DCGs-based remote VD environment tailored for EEA anatomy, and evaluate its educational use in neurosurgical residents.

Methods: From our previously reported high-fidelity skull base 3DCGs, the structures necessary to reproduce the sellar and parasellar regions were selected, and EEA-specific 3DCGs were constructed. The model was visualized on an interactive simulation system, and provided remotely to 14 neurosurgical residents (postgraduate years 3–7). After a 20-min anatomical learning session, participants completed a satisfaction evaluation and a confidence evaluation. Both used a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Satisfaction was assessed via five items; “Overall satisfaction”, “Realism of the VD experience”, “Visual fidelity of anatomical structures”, “Usefulness of the system for learning EEA anatomy”, and “Ease of use and interaction with the interface”. Confidence was assessed via five items; “Understanding 3D anatomical relationships”, “Identifying critical anatomical structures”, “Performing safe and accurate anatomical approaches”, “Applying anatomical knowledge to clinical scenarios”, and “Independently continuing anatomical learning using the system”.

Results: The finalized 3DCGs consisted of 304 parts and approximately 18.6 million polygons. Building upon this, an interactive VD environment specialized for the EEA was successfully constructed. All satisfaction and confidence items demonstrated median scores of 4 or higher. The highest satisfaction ratings were for “Visual fidelity of anatomical structures” and “Usefulness of the system for learning EEA anatomy”, both with a median of 5 (interquartile range 4–5). Confidence evaluation also yielded consistently high ratings, with “Independently continuing anatomical learning using the system” receiving the highest score (median 5, interquartile range 5–5). Open-ended feedback was largely positive, highlighting “Facilitation of 3D understanding”, “Clarity of anatomical structures”, and “Learning with a sense of play”. Suggested improvements included “Delays in response due to remote operation” and “Limited time to review the numerous displayed structures”.

Conclusion: The above-described newly developed remote VD environment provided high satisfaction and confidence among neurosurgical residents. By overcoming some inherent limitations of textbooks, lectures, videos, and cadaveric dissection, high-fidelity 3DCGs-based VD represents a promising next-generation option for skull base anatomy training, enabling flexible, interactive, and sustainable remote education.

 

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